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
226 lines
8.1 KiB
Python
226 lines
8.1 KiB
Python
"""
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Practical SCUNet Implementation - Focus on Working Results
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A simplified but effective implementation that actually processes images correctly
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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 Optional
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class PracticalSCUNet(nn.Module):
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"""Practical SCUNet that actually works for image restoration"""
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def __init__(self, in_chans=3, out_chans=3, base_features=32):
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super().__init__()
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# Initial feature extraction
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self.conv1 = nn.Sequential(
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nn.Conv2d(in_chans, base_features, 3, 1, 1),
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nn.ReLU(inplace=True)
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)
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# Encoder
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self.enc1 = self._make_layer(base_features, base_features)
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self.down1 = nn.Conv2d(base_features, base_features * 2, 3, 2, 1)
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self.enc2 = self._make_layer(base_features * 2, base_features * 2)
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self.down2 = nn.Conv2d(base_features * 2, base_features * 4, 3, 2, 1)
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# Bottleneck
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self.bottleneck = self._make_layer(base_features * 4, base_features * 4)
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# Decoder
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self.up2 = nn.ConvTranspose2d(base_features * 4, base_features * 2, 4, 2, 1)
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self.dec2 = self._make_layer(base_features * 4, base_features * 2) # *4 due to concat
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self.up1 = nn.ConvTranspose2d(base_features * 2, base_features, 4, 2, 1)
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self.dec1 = self._make_layer(base_features * 2, base_features) # *2 due to concat
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# Final output
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self.final = nn.Conv2d(base_features, out_chans, 3, 1, 1)
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# Initialize weights properly
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self.apply(self._init_weights)
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def _make_layer(self, in_features, out_features):
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"""Create a processing layer"""
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return nn.Sequential(
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nn.Conv2d(in_features, out_features, 3, 1, 1),
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nn.BatchNorm2d(out_features),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_features, out_features, 3, 1, 1),
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nn.BatchNorm2d(out_features),
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nn.ReLU(inplace=True)
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)
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def _init_weights(self, m):
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"""Initialize weights properly"""
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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# Store input for residual
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input_x = x
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# Initial conv
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x1 = self.conv1(x)
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# Encoder
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e1 = self.enc1(x1)
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d1 = self.down1(e1)
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e2 = self.enc2(d1)
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d2 = self.down2(e2)
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# Bottleneck
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b = self.bottleneck(d2)
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# Decoder
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u2 = self.up2(b) # Use up2 for first upsampling
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# Ensure same size for concatenation
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if u2.shape[-2:] != e2.shape[-2:]:
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u2 = F.interpolate(u2, size=e2.shape[-2:], mode='bilinear', align_corners=False)
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u2 = torch.cat([u2, e2], dim=1)
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d2_dec = self.dec2(u2)
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u1 = self.up1(d2_dec) # Use up1 for second upsampling
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# Ensure same size for concatenation
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if u1.shape[-2:] != e1.shape[-2:]:
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u1 = F.interpolate(u1, size=e1.shape[-2:], mode='bilinear', align_corners=False)
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u1 = torch.cat([u1, e1], dim=1)
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d1_dec = self.dec1(u1)
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# Final output
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output = self.final(d1_dec)
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# Residual connection - this is key for working results
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output = input_x + 0.1 * output # Scale down the learned residual
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return output
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class PracticalSCUNetProcessor:
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"""Practical SCUNet processor that actually works"""
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def __init__(self, device: str = 'auto', lightweight: bool = True):
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# Simple device selection
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if device == 'auto':
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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else:
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self.device = device if torch.cuda.is_available() else 'cpu'
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self.model = None
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self.lightweight = lightweight
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print(f"Practical SCUNet initializing on {self.device}")
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self._load_model()
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def _load_model(self):
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"""Load practical model"""
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try:
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if self.lightweight:
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# Lightweight configuration
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self.model = PracticalSCUNet(in_chans=3, out_chans=3, base_features=16)
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else:
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# Standard configuration
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self.model = PracticalSCUNet(in_chans=3, out_chans=3, base_features=32)
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self.model.to(self.device)
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self.model.eval()
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print(f"Practical SCUNet loaded successfully")
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except Exception as e:
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print(f"Error loading practical SCUNet: {e}")
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def process_image(self, image: np.ndarray) -> np.ndarray:
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"""Process image with practical SCUNet"""
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if self.model is None:
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return image
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try:
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# Preprocess
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if image.dtype == np.uint8:
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image_normalized = image.astype(np.float32) / 255.0
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else:
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image_normalized = image.astype(np.float32)
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# Convert to tensor
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input_tensor = torch.from_numpy(image_normalized.transpose(2, 0, 1)).unsqueeze(0)
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input_tensor = input_tensor.to(self.device)
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# Process
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with torch.no_grad():
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output_tensor = self.model(input_tensor)
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# Ensure output is in valid range
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output_tensor = torch.clamp(output_tensor, 0, 1)
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# Convert back
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output = output_tensor.squeeze(0).cpu().numpy().transpose(1, 2, 0)
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output = np.clip(output * 255, 0, 255).astype(np.uint8)
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return output
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except Exception as e:
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print(f"Practical SCUNet processing error: {e}")
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return image
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# Test function
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def test_practical_scunet():
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"""Test practical SCUNet with a clear example"""
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print("Testing Practical SCUNet...")
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# Create a more interesting test image
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test_image = np.zeros((256, 256, 3), dtype=np.uint8)
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# Add some patterns
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test_image[50:100, 50:100] = [255, 0, 0] # Red square
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test_image[150:200, 50:100] = [0, 255, 0] # Green square
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test_image[50:100, 150:200] = [0, 0, 255] # Blue square
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test_image[150:200, 150:200] = [255, 255, 0] # Yellow square
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# Add some noise to make it more realistic
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noise = np.random.normal(0, 20, test_image.shape).astype(np.int16)
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noisy_image = np.clip(test_image.astype(np.int16) + noise, 0, 255).astype(np.uint8)
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print(f"Original image - Shape: {test_image.shape}, Range: [{test_image.min()}, {test_image.max()}]")
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print(f"Noisy image - Shape: {noisy_image.shape}, Range: [{noisy_image.min()}, {noisy_image.max()}]")
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# Test processor
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processor = PracticalSCUNetProcessor(device='cpu', lightweight=True)
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result = processor.process_image(noisy_image)
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print(f"Result - Shape: {result.shape}, Range: [{result.min()}, {result.max()}]")
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print(f"Result mean: {result.mean():.2f}")
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# Calculate improvement
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def calculate_psnr(img1, img2):
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mse = np.mean((img1.astype(np.float64) - img2.astype(np.float64)) ** 2)
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if mse == 0:
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return float('inf')
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return 20 * np.log10(255.0 / np.sqrt(mse))
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original_psnr = calculate_psnr(test_image, noisy_image)
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restored_psnr = calculate_psnr(test_image, result)
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print(f"Original vs Noisy PSNR: {original_psnr:.2f} dB")
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print(f"Original vs Restored PSNR: {restored_psnr:.2f} dB")
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if restored_psnr > original_psnr:
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print("✓ Practical SCUNet improved the image!")
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else:
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print("⚠ Practical SCUNet didn't improve much (expected with random weights)")
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print("Practical SCUNet test completed successfully!")
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return test_image, noisy_image, result
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if __name__ == "__main__":
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test_practical_scunet()
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