IMAGE DENOISING IN DIP
Image denoising is a critical topic in digital image processing, where the goal is to remove unwanted noise from images while preserving important details such as edges and textures. Noise can be introduced during image acquisition due to various factors like sensor limitations, transmission errors, or environmental conditions.
Types of Noise in Images:
1. Gaussian Noise:
Caused by thermal energy or poor lighting conditions.
The noise has a normal (Gaussian) distribution with a bell-shaped probability density function.
2. Salt-and-Pepper Noise:
Appears as randomly occurring white and black pixels.
Typically introduced during transmission or by faulty sensors.
3. Poisson Noise:
Often referred to as shot noise.
Occurs due to the random nature of photon counting during image acquisition.
4. Speckle Noise:
Common in radar and ultrasound images.
Results from the interference of coherent waves reflected from various surfaces.
Common Image Denoising Techniques:
1. Gaussian Filtering:
A low-pass filter that uses a Gaussian kernel to smooth images.
Effective for removing Gaussian noise but tends to blur the image, leading to loss of edge details.
2. Median Filtering:
A nonlinear filter that replaces each pixel’s value with the median of its neighboring pixels.
Effective for removing salt-and-pepper noise while preserving edges better than Gaussian filtering.
3. Bilateral Filtering:
A nonlinear, edge-preserving, and noise-reducing smoothing filter.
Combines Gaussian smoothing with an additional weighting based on pixel intensity differences to preserve edges.
4. Wiener Filter:
A linear filter that minimizes the mean square error between the estimated image and the original one.
Assumes that the noise and signal have known spectral characteristics.
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5. Wavelet Transform-Based Denoising:
Involves transforming the image into a wavelet domain where noise and signal can be separated more effectively.
Thresholding techniques are then used to suppress noise in the wavelet coefficients.
6. Non-Local Means (NLM):
A technique where the noise in a pixel is reduced by averaging pixels with similar structures across the image.
Effective in preserving textures but computationally intensive.
7. Total Variation (TV) Denoising:
Minimizes the total variation of the image, promoting smooth regions while preserving edges.
Widely used in medical and remote sensing images.
8. Deep Learning-Based Denoising:
Uses convolutional neural networks (CNNs) and autoencoders to learn noise patterns and remove them from the image.
Examples include DnCNN and UNet models, which outperform traditional methods for complex noise patterns.
Evaluation Metrics for Denoising:
1. Peak Signal-to-Noise Ratio (PSNR):
Measures the ratio between the maximum possible value of the image and the noise power.
Higher PSNR values indicate better image quality.
2. Structural Similarity Index (SSIM):
Evaluates the similarity between the original and denoised image, focusing on structural information like luminance, contrast, and texture.
Applications of Image Denoising:
Medical Imaging: Enhancing the quality of X-rays, MRIs, and CT scans by removing noise without losing diagnostic details.
Photography: Reducing noise in low-light photography, improving image clarity.
Remote Sensing: Cleaning satellite images affected by environmental noise.
Video Processing: Noise reduction in surveillance footage for better clarity and object recognition.