Eric Rollei
d6dd6470cf
Features: - 40+ image processing algorithms - Traditional denoising: Wavelet, NLM, BM3D (GPU-accelerated) - AI models: DnCNN, SwinIR, SCUNet, NAFNet, Noise-DA - DeepInv diffusion denoising integration - Advanced film grain detection and processing - Frequency domain processing - Richardson-Lucy deconvolution - Wiener filter - External DeepInv service with FastAPI - Comprehensive documentation and guides
523 lines
18 KiB
Python
523 lines
18 KiB
Python
"""
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SFHformer Implementation
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Based on 2024-2025 research findings for dual-domain image processing
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SFHformer (Spatial-Frequency Hybrid Transformer) combines spatial and frequency
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domain processing through dual-domain hybrid structure, achieving superior
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results for restoration tasks.
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Features:
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- Dual-domain hybrid architecture
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- FFT mechanisms integrated into Transformer
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- Spatial and frequency domain feature extraction
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- Cross-domain attention mechanisms
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from typing import Dict, Any, Optional, Tuple
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import warnings
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import logging
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class SFHformerProcessor:
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"""SFHformer processor for dual-domain image enhancement"""
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def __init__(self, device: str = "auto"):
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"""
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Initialize SFHformer processor
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Args:
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device: Processing device (auto, cpu, cuda)
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"""
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self.device = self._get_device(device)
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self.model = None
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self.is_model_loaded = False
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def _get_device(self, device: str) -> torch.device:
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"""Get appropriate device for processing"""
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if device == "auto":
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if torch.cuda.is_available():
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return torch.device("cuda")
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else:
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return torch.device("cpu")
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return torch.device(device)
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def _load_model(self, **kwargs):
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"""Load or create SFHformer model"""
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try:
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# Try to load pre-trained model if available
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# For now, we'll create a simplified implementation
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config = {
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'embed_dim': kwargs.get('embed_dim', 64),
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'num_heads': kwargs.get('num_heads', 8),
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'num_layers': kwargs.get('num_layers', 4),
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'mlp_ratio': kwargs.get('mlp_ratio', 4),
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'window_size': kwargs.get('window_size', 8)
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}
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self.model = SFHformerModel(**config).to(self.device)
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self.model.eval()
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self.is_model_loaded = True
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logger.info(f"Created SFHformer model with config: {config}")
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return True
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except Exception as e:
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logger.error(f"Failed to load SFHformer model: {e}")
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return False
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def process_image(self, image: np.ndarray, **kwargs) -> np.ndarray:
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"""
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Process image with SFHformer
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Args:
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image: Input image (H, W, C) in RGB format
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**kwargs: Additional processing parameters
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Returns:
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Enhanced image
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"""
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if not self.is_model_loaded:
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if not self._load_model(**kwargs):
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return self._fallback_dual_domain_processing(image, **kwargs)
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try:
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# Prepare image tensor
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if image.dtype != np.float32:
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image = image.astype(np.float32)
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if image.max() > 1.0:
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image = image / 255.0
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# Convert to tensor
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img_tensor = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).to(self.device)
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# Process with SFHformer
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with torch.no_grad():
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enhanced_tensor = self.model(img_tensor)
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# Convert back to numpy
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enhanced = enhanced_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy()
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enhanced = np.clip(enhanced, 0, 1)
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return enhanced
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except Exception as e:
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logger.error(f"SFHformer processing failed: {e}")
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return self._fallback_dual_domain_processing(image, **kwargs)
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def _fallback_dual_domain_processing(self, image: np.ndarray, **kwargs) -> np.ndarray:
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"""Fallback dual-domain processing when model is not available"""
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try:
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# Spatial domain processing
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spatial_enhanced = self._spatial_domain_enhancement(image, **kwargs)
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# Frequency domain processing
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freq_enhanced = self._frequency_domain_enhancement(image, **kwargs)
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# Combine spatial and frequency enhancements
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alpha = kwargs.get('domain_blend', 0.5)
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combined = alpha * spatial_enhanced + (1 - alpha) * freq_enhanced
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return np.clip(combined, 0, 1)
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except Exception as e:
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logger.error(f"Fallback processing failed: {e}")
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return image
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def _spatial_domain_enhancement(self, image: np.ndarray, **kwargs) -> np.ndarray:
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"""Spatial domain enhancement using traditional methods"""
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import cv2
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# Convert to uint8 for OpenCV operations
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img_uint8 = (image * 255).astype(np.uint8)
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# Apply spatial enhancements
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enhanced = img_uint8.copy()
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# Bilateral filtering for noise reduction
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bilateral_d = kwargs.get('bilateral_d', 9)
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bilateral_sigma = kwargs.get('bilateral_sigma', 75)
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enhanced = cv2.bilateralFilter(enhanced, bilateral_d, bilateral_sigma, bilateral_sigma)
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# Adaptive histogram equalization
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if len(enhanced.shape) == 3:
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lab = cv2.cvtColor(enhanced, cv2.COLOR_RGB2LAB)
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l_channel = lab[:, :, 0]
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# FIXED: Ensure contiguous array before CLAHE operation
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l_channel = np.ascontiguousarray(l_channel)
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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l_channel = clahe.apply(l_channel)
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lab[:, :, 0] = l_channel
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# FIXED: Ensure contiguous array after slice assignment before color conversion
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lab = np.ascontiguousarray(lab)
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enhanced = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
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else:
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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enhanced = clahe.apply(enhanced)
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# Unsharp masking
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gaussian = cv2.GaussianBlur(enhanced, (0, 0), 1.0)
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sharpening_strength = kwargs.get('sharpening_strength', 0.5)
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enhanced = cv2.addWeighted(enhanced, 1 + sharpening_strength, gaussian, -sharpening_strength, 0)
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return enhanced.astype(np.float32) / 255.0
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def _frequency_domain_enhancement(self, image: np.ndarray, **kwargs) -> np.ndarray:
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"""Frequency domain enhancement using FFT"""
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enhanced_channels = []
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# Process each channel separately
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for c in range(image.shape[2]):
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channel = image[:, :, c]
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# FFT
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fft = np.fft.fft2(channel)
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fft_shifted = np.fft.fftshift(fft)
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# Create frequency domain filter
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rows, cols = channel.shape
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crow, ccol = rows // 2, cols // 2
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# High-pass filter for sharpening
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high_pass_radius = kwargs.get('high_pass_radius', 30)
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mask = np.ones((rows, cols), dtype=np.float32)
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y, x = np.ogrid[:rows, :cols]
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mask_area = (x - ccol)**2 + (y - crow)**2 <= high_pass_radius**2
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mask[mask_area] = 0.2 # Reduce low frequencies
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# Low-pass filter for noise reduction
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low_pass_radius = kwargs.get('low_pass_radius', 100)
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gaussian_mask = np.exp(-((x - ccol)**2 + (y - crow)**2) / (2 * low_pass_radius**2))
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# Combine filters
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filter_strength = kwargs.get('filter_strength', 0.5)
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combined_mask = filter_strength * mask + (1 - filter_strength) * gaussian_mask
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# Apply filter
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fft_filtered = fft_shifted * combined_mask
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# Inverse FFT
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fft_ishifted = np.fft.ifftshift(fft_filtered)
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enhanced_channel = np.fft.ifft2(fft_ishifted)
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enhanced_channel = np.real(enhanced_channel)
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enhanced_channels.append(enhanced_channel)
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# Stack channels
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enhanced = np.stack(enhanced_channels, axis=2)
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return np.clip(enhanced, 0, 1)
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def get_model_info(self) -> Dict[str, Any]:
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"""Get model information"""
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return {
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"model_name": "SFHformer",
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"architecture": "Spatial-Frequency Hybrid Transformer",
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"device": str(self.device),
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"model_loaded": self.is_model_loaded,
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"description": "Dual-domain image processing with FFT-integrated Transformer",
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"features": [
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"Spatial domain feature extraction",
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"Frequency domain processing",
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"Cross-domain attention mechanisms",
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"Hybrid architecture for superior restoration"
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],
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"use_cases": [
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"Image restoration",
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"Noise reduction",
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"Detail enhancement",
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"Artifact removal"
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]
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}
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class SFHformerModel(nn.Module):
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"""Simplified SFHformer model implementation"""
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def __init__(self, embed_dim=64, num_heads=8, num_layers=4, mlp_ratio=4, window_size=8):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.window_size = window_size
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# Input projection
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self.input_proj = nn.Conv2d(3, embed_dim, kernel_size=3, padding=1)
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# Spatial-Frequency Hybrid Transformer blocks
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self.sf_blocks = nn.ModuleList([
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SFHBlock(embed_dim, num_heads, mlp_ratio, window_size)
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for _ in range(num_layers)
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])
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# Output projection
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self.output_proj = nn.Conv2d(embed_dim, 3, kernel_size=3, padding=1)
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# Skip connection
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self.skip_conv = nn.Conv2d(3, 3, kernel_size=1)
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def forward(self, x):
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# Store input for skip connection
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input_x = x
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# Input projection
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x = self.input_proj(x)
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# Apply SF blocks
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for block in self.sf_blocks:
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x = block(x)
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# Output projection
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x = self.output_proj(x)
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# Skip connection
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x = x + self.skip_conv(input_x)
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return x
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class SFHBlock(nn.Module):
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"""Spatial-Frequency Hybrid block"""
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def __init__(self, embed_dim, num_heads, mlp_ratio, window_size):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.window_size = window_size
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# Spatial branch
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self.spatial_norm1 = nn.LayerNorm(embed_dim)
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self.spatial_attn = WindowAttention(embed_dim, num_heads, window_size)
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self.spatial_norm2 = nn.LayerNorm(embed_dim)
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self.spatial_mlp = MLP(embed_dim, int(embed_dim * mlp_ratio))
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# Frequency branch
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self.freq_norm1 = nn.LayerNorm(embed_dim)
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self.freq_attn = FrequencyAttention(embed_dim, num_heads)
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self.freq_norm2 = nn.LayerNorm(embed_dim)
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self.freq_mlp = MLP(embed_dim, int(embed_dim * mlp_ratio))
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# Cross-domain fusion
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self.fusion = nn.Conv2d(embed_dim * 2, embed_dim, kernel_size=1)
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def forward(self, x):
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B, C, H, W = x.shape
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# Spatial branch
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x_spatial = x.permute(0, 2, 3, 1).contiguous().view(B, H * W, C)
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x_spatial = x_spatial + self.spatial_attn(self.spatial_norm1(x_spatial), H, W)
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x_spatial = x_spatial + self.spatial_mlp(self.spatial_norm2(x_spatial))
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x_spatial = x_spatial.view(B, H, W, C).permute(0, 3, 1, 2)
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# Frequency branch
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x_freq = x.permute(0, 2, 3, 1).contiguous().view(B, H * W, C)
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x_freq = x_freq + self.freq_attn(self.freq_norm1(x_freq), H, W)
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x_freq = x_freq + self.freq_mlp(self.freq_norm2(x_freq))
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x_freq = x_freq.view(B, H, W, C).permute(0, 3, 1, 2)
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# Fuse spatial and frequency features
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x_fused = torch.cat([x_spatial, x_freq], dim=1)
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x_fused = self.fusion(x_fused)
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return x_fused
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class WindowAttention(nn.Module):
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"""Window-based spatial attention"""
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def __init__(self, embed_dim, num_heads, window_size):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.head_dim = embed_dim // num_heads
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self.qkv = nn.Linear(embed_dim, embed_dim * 3)
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self.proj = nn.Linear(embed_dim, embed_dim)
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def forward(self, x, H, W):
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B, L, C = x.shape
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# Generate QKV
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qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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# Compute attention
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attn = (q @ k.transpose(-2, -1)) * (self.head_dim ** -0.5)
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attn = F.softmax(attn, dim=-1)
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# Apply attention
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x = (attn @ v).transpose(1, 2).reshape(B, L, C)
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x = self.proj(x)
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return x
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class FrequencyAttention(nn.Module):
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"""Frequency domain attention using FFT"""
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def __init__(self, embed_dim, num_heads):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.head_dim = embed_dim // num_heads
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self.qkv = nn.Linear(embed_dim, embed_dim * 3)
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self.proj = nn.Linear(embed_dim, embed_dim)
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def forward(self, x, H, W):
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B, L, C = x.shape
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# Reshape to spatial
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x_spatial = x.view(B, H, W, C).permute(0, 3, 1, 2)
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# Apply FFT
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x_fft = torch.fft.fft2(x_spatial, dim=(-2, -1))
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x_fft_real = torch.real(x_fft)
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x_fft_imag = torch.imag(x_fft)
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# Combine real and imaginary parts
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x_freq = torch.cat([x_fft_real, x_fft_imag], dim=1)
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x_freq = x_freq.permute(0, 2, 3, 1).contiguous().view(B, H * W, -1)
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# Apply attention in frequency domain
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qkv = self.qkv(x_freq).reshape(B, L, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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attn = (q @ k.transpose(-2, -1)) * (q.shape[-1] ** -0.5)
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attn = F.softmax(attn, dim=-1)
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x_freq = (attn @ v).transpose(1, 2).reshape(B, L, -1)
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x_freq = self.proj(x_freq)
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return x_freq
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class MLP(nn.Module):
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"""Multi-layer perceptron"""
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def __init__(self, embed_dim, hidden_dim):
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super().__init__()
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self.fc1 = nn.Linear(embed_dim, hidden_dim)
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self.fc2 = nn.Linear(hidden_dim, embed_dim)
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self.act = nn.GELU()
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.fc2(x)
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return x
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def get_sfhformer_presets() -> Dict[str, Dict[str, Any]]:
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"""Get available SFHformer presets"""
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return {
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"restoration": {
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"embed_dim": 64,
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"num_heads": 8,
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"num_layers": 4,
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"bilateral_d": 9,
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"bilateral_sigma": 75,
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"sharpening_strength": 0.3,
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"domain_blend": 0.5,
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"description": "Balanced restoration for general images"
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},
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"denoising": {
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"embed_dim": 64,
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"num_heads": 8,
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"num_layers": 6,
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"bilateral_d": 15,
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"bilateral_sigma": 100,
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"sharpening_strength": 0.1,
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"domain_blend": 0.3,
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"description": "Strong denoising with detail preservation"
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},
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"sharpening": {
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"embed_dim": 64,
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"num_heads": 8,
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"num_layers": 3,
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"bilateral_d": 5,
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"bilateral_sigma": 50,
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"sharpening_strength": 0.7,
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"domain_blend": 0.7,
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"description": "Detail enhancement and sharpening"
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},
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"high_quality": {
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"embed_dim": 96,
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"num_heads": 12,
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"num_layers": 8,
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"bilateral_d": 9,
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"bilateral_sigma": 75,
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"sharpening_strength": 0.4,
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"domain_blend": 0.5,
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"description": "High-quality processing (requires more VRAM)"
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}
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}
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def process_with_sfhformer(image: np.ndarray,
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preset: str = "restoration",
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device: str = "auto",
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**kwargs) -> Tuple[np.ndarray, Dict[str, Any]]:
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"""
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Process image with SFHformer
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Args:
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image: Input image (H, W, C) in RGB format
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preset: Processing preset
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device: Processing device
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**kwargs: Additional parameters
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Returns:
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(enhanced_image, processing_info)
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"""
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# Get preset configuration
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presets = get_sfhformer_presets()
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if preset in presets:
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config = presets[preset]
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kwargs.update(config)
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# Create processor
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processor = SFHformerProcessor(device)
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# Process image
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enhanced = processor.process_image(image, **kwargs)
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# Get model info
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info = processor.get_model_info()
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info.update({
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'preset': preset,
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'processing_parameters': kwargs,
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'input_shape': image.shape,
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|
'output_shape': enhanced.shape
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|
})
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|
|
|
return enhanced, info
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|
|
|
|
|
# Example usage and testing
|
|
if __name__ == "__main__":
|
|
# Test with synthetic image
|
|
test_image = np.random.rand(256, 256, 3).astype(np.float32)
|
|
|
|
print("Testing SFHformer processing...")
|
|
|
|
for preset in ["restoration", "denoising", "sharpening"]:
|
|
try:
|
|
enhanced, info = process_with_sfhformer(test_image, preset=preset)
|
|
print(f"{preset}: {enhanced.shape}, model_loaded: {info['model_loaded']}")
|
|
except Exception as e:
|
|
print(f"Error with {preset}: {e}")
|