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310 lines
10 KiB
Python
310 lines
10 KiB
Python
"""
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Historical Newspaper Image Cleaning Pipeline
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This module provides functions to clean and enhance scanned historical newspaper images
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by reducing noise, improving contrast, and sharpening text for better readability.
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"""
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import cv2
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import numpy as np
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from PIL import Image, ImageEnhance
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import os
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import argparse
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from pathlib import Path
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class NewspaperImageCleaner:
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"""
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Image processing pipeline specifically designed for historical newspaper scans.
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"""
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def __init__(self, config=None):
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"""Initialize with default or custom configuration."""
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self.config = config or self._default_config()
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def _default_config(self):
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"""Default processing parameters optimized for newspaper scans."""
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return {
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'bilateral_d': 9, # Neighborhood diameter for bilateral filter
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'bilateral_sigma_color': 75, # Filter sigma in color space
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'bilateral_sigma_space': 75, # Filter sigma in coordinate space
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'clahe_clip_limit': 2.0, # Contrast limiting for CLAHE
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'clahe_grid_size': (8, 8), # CLAHE grid size
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'gamma': 1.2, # Gamma correction value
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'denoise_h': 10, # Denoising filter strength
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'morph_kernel_size': 2, # Morphological operation kernel size
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'unsharp_amount': 1.5, # Unsharp masking amount
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'unsharp_radius': 1.0, # Unsharp masking radius
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'unsharp_threshold': 0, # Unsharp masking threshold
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}
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def reduce_noise(self, image):
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"""
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Apply noise reduction techniques to remove speckles and JPEG artifacts.
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Args:
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image: Input BGR image
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Returns:
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Denoised image
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"""
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# Bilateral filter - preserves edges while reducing noise
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bilateral = cv2.bilateralFilter(
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image,
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self.config['bilateral_d'],
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self.config['bilateral_sigma_color'],
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self.config['bilateral_sigma_space']
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)
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# Non-local means denoising for better noise reduction
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if len(image.shape) == 3:
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# Color image
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denoised = cv2.fastNlMeansDenoisingColored(
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bilateral, None,
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self.config['denoise_h'],
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self.config['denoise_h'],
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7, 21
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)
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else:
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# Grayscale image
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denoised = cv2.fastNlMeansDenoising(
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bilateral, None,
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self.config['denoise_h'],
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7, 21
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)
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return denoised
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def enhance_contrast(self, image):
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"""
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Improve image contrast using CLAHE and gamma correction.
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Args:
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image: Input BGR image
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Returns:
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Contrast-enhanced image
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"""
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# Convert to LAB color space for better contrast processing
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if len(image.shape) == 3:
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lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
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l_channel, a_channel, b_channel = cv2.split(lab)
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else:
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l_channel = image
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# Apply CLAHE (Contrast Limited Adaptive Histogram Equalization)
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clahe = cv2.createCLAHE(
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clipLimit=self.config['clahe_clip_limit'],
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tileGridSize=self.config['clahe_grid_size']
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)
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l_channel = clahe.apply(l_channel)
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# Reconstruct image
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if len(image.shape) == 3:
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enhanced = cv2.merge([l_channel, a_channel, b_channel])
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enhanced = cv2.cvtColor(enhanced, cv2.COLOR_LAB2BGR)
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else:
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enhanced = l_channel
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# Apply gamma correction
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gamma = self.config['gamma']
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inv_gamma = 1.0 / gamma
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table = np.array([((i / 255.0) ** inv_gamma) * 255
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for i in np.arange(0, 256)]).astype("uint8")
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enhanced = cv2.LUT(enhanced, table)
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return enhanced
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def clean_background(self, image):
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"""
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Remove small artifacts and clean background noise.
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Args:
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image: Input image
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Returns:
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Background-cleaned image
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"""
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# Convert to grayscale for morphological operations
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image
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# Morphological opening to remove small noise
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kernel = np.ones((self.config['morph_kernel_size'],
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self.config['morph_kernel_size']), np.uint8)
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# Opening (erosion followed by dilation)
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opened = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
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# If original was color, apply the mask
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if len(image.shape) == 3:
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# Create a mask and apply it to the original color image
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mask = opened > 0
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result = image.copy()
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result[~mask] = [255, 255, 255] # Set background to white
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return result
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else:
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return opened
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def sharpen_image(self, image):
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"""
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Apply unsharp masking to enhance text clarity.
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Args:
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image: Input image
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Returns:
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Sharpened image
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"""
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# Convert to float for processing
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float_img = image.astype(np.float32) / 255.0
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# Create Gaussian blur
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radius = self.config['unsharp_radius']
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sigma = radius / 3.0
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blurred = cv2.GaussianBlur(float_img, (0, 0), sigma)
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# Unsharp masking
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amount = self.config['unsharp_amount']
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sharpened = float_img + amount * (float_img - blurred)
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# Threshold and clamp
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threshold = self.config['unsharp_threshold'] / 255.0
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sharpened = np.where(np.abs(float_img - blurred) < threshold,
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float_img, sharpened)
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sharpened = np.clip(sharpened, 0.0, 1.0)
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return (sharpened * 255).astype(np.uint8)
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def process_image(self, image_path, output_path=None, steps=None):
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"""
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Process a single image through the complete pipeline.
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Args:
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image_path: Path to input image
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output_path: Path for output image (optional)
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steps: List of processing steps to apply (optional)
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Returns:
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Processed image array
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"""
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if steps is None:
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steps = ['denoise', 'contrast', 'background', 'sharpen']
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# Load image
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image = cv2.imread(str(image_path))
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if image is None:
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raise ValueError(f"Could not load image: {image_path}")
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original = image.copy()
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# Apply processing steps
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if 'denoise' in steps:
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print(f"Applying noise reduction...")
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image = self.reduce_noise(image)
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if 'contrast' in steps:
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print(f"Enhancing contrast...")
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image = self.enhance_contrast(image)
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if 'background' in steps:
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print(f"Cleaning background...")
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image = self.clean_background(image)
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if 'sharpen' in steps:
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print(f"Sharpening image...")
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image = self.sharpen_image(image)
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# Save output if path provided
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if output_path:
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cv2.imwrite(str(output_path), image)
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print(f"Processed image saved to: {output_path}")
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return image, original
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def process_directory(self, input_dir, output_dir, extensions=None):
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"""
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Process all images in a directory.
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Args:
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input_dir: Input directory path
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output_dir: Output directory path
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extensions: List of file extensions to process
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"""
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if extensions is None:
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extensions = ['.jpg', '.jpeg', '.png', '.tif', '.tiff']
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input_path = Path(input_dir)
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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for file_path in input_path.iterdir():
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if file_path.suffix.lower() in extensions:
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print(f"\nProcessing: {file_path.name}")
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output_file = output_path / f"cleaned_{file_path.name}"
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try:
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self.process_image(file_path, output_file)
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except Exception as e:
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print(f"Error processing {file_path.name}: {str(e)}")
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print(f"\nBatch processing completed. Results in: {output_dir}")
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def create_comparison_image(original, processed, output_path):
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"""
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Create a side-by-side comparison image.
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Args:
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original: Original image array
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processed: Processed image array
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output_path: Path to save comparison
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"""
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# Resize images to same height if needed
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h1, w1 = original.shape[:2]
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h2, w2 = processed.shape[:2]
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if h1 != h2:
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height = min(h1, h2)
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original = cv2.resize(original, (int(w1 * height / h1), height))
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processed = cv2.resize(processed, (int(w2 * height / h2), height))
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# Create side-by-side comparison
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comparison = np.hstack([original, processed])
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cv2.imwrite(str(output_path), comparison)
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print(f"Comparison saved to: {output_path}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Clean historical newspaper images")
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parser.add_argument("input", help="Input image or directory path")
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parser.add_argument("-o", "--output", help="Output path")
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parser.add_argument("-d", "--directory", action="store_true",
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help="Process entire directory")
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parser.add_argument("-c", "--comparison", action="store_true",
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help="Create before/after comparison")
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parser.add_argument("--steps", nargs="+",
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choices=['denoise', 'contrast', 'background', 'sharpen'],
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default=['denoise', 'contrast', 'background', 'sharpen'],
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help="Processing steps to apply")
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args = parser.parse_args()
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cleaner = NewspaperImageCleaner()
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if args.directory:
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output_dir = args.output or "cleaned_images"
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cleaner.process_directory(args.input, output_dir)
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else:
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output_path = args.output
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if not output_path:
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input_path = Path(args.input)
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output_path = input_path.parent / f"cleaned_{input_path.name}"
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processed, original = cleaner.process_image(args.input, output_path, args.steps)
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if args.comparison:
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comparison_path = Path(output_path).parent / f"comparison_{Path(args.input).name}"
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create_comparison_image(original, processed, comparison_path) |