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
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
import cv2
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import numpy as np
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from pathlib import Path
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from tqdm import tqdm
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def add_film_grain(image, grain_strength=0.05):
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"""Add realistic film grain to image"""
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noise = np.random.normal(0, grain_strength, image.shape)
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# Add spatial correlation
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kernel = cv2.getGaussianKernel(3, 0.5)
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kernel = kernel @ kernel.T
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for i in range(image.shape[2]):
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noise[:, :, i] = cv2.filter2D(noise[:, :, i], -1, kernel)
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# Intensity-dependent noise
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intensity_factor = 1.0 + 0.5 * (image / 255.0)
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noise = noise * intensity_factor
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noisy = image + noise
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noisy = np.clip(noisy, 0, 255).astype(np.uint8)
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return noisy
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# Process all images
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clean_dir = Path("my_training_data/clean")
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noisy_dir = Path("my_training_data/noisy")
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noisy_dir.mkdir(exist_ok=True)
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for img_path in tqdm(list(clean_dir.glob("*.png"))):
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img = cv2.imread(str(img_path))
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noisy = add_film_grain(img, grain_strength=15.0) # Adjust strength as needed
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cv2.imwrite(str(noisy_dir / img_path.name), noisy)
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print("✓ Synthetic grain added to all images!") |