The Image Degradation / Restoration Process

The image degradation and restoration process involves several key steps and techniques aimed at improving the quality of degraded images. Here’s an overview of the process:

Image Degradation

Image degradation refers to the loss of image quality due to various factors, which can include

Noise: Abrupt changes in an image's brightness or color information, frequently brought on by transmission mistakes, environmental factors, or sensor limits.
Blur: Sharpness loss brought on by atmospheric factors, out-of-focus lenses, or camera movement.
Compression artifacts: Quality loss brought on by lossy compression methods, such JPEG.
Geometric Distortions: The stretching or warping of a picture due to defects in the lens or camera angle.
Additional Artifacts: Like ghosting, ringing, or color bleeding.

Image Restoration

Image restoration aims to reverse the degradation process and recover the original image quality as much as possible. The restoration process typically involves several techniques and steps:

Noise Reduction

Spatial Filtering: Noise reduction with edge preservation using methods such as bilateral filtering, median filtering, and Gaussian smoothing.
Frequency domain filtering: Methods to eliminate particular frequency components linked to noise, such as Wiener or notch filtering.
 

De-blurring

Inverse Filtering: Recovering the original image with inverse filtering involves applying the blur function's inverse.
Wiener Filtering: A method of deconvolution that considers both noise and blur.
Blind Deconvolution: When the blur function is unknown, blind deconvolution involves estimating both the blur and the original image.

Compression Artifact Removal

Denoising Algorithms: Techniques like Non-Local Means (NLM) or BM3D to reduce blockiness and ringing artifacts.
Deep Learning Methods: Using neural networks to learn and remove compression artifacts.

Geometric Correction

Transformation Techniques: To fix distortions, use projective or affine transformations.
Warping Algorithms: These algorithms fix non-linear distortions by interpolating and using control points.

Enhancement Techniques

Contrast Enhancement: Techniques to increase contrast include contrast-limited adaptive histogram equalization (CLAHE) and histogram equalization.
Sharpening: Using methods to bring out details and edges, such as unsharp masking.

Workflow of Image Restoration Process

•  ProbIem identification : Dentify the problem by determining the kind and degree of degradation.
• Model Selection: Select the right restoration methods according to the kind of degradation.
• Parameter tuning: To maximize performance, modify the parameters of the selected models.
• Application: Restore the damaged image using the restoration techniques.
• Evaluation: Use both subjective assessment and objective measurements, such as PSNR and SSIM, to gauge the quality of the restored image.

By following these steps and utilizing advanced techniques, the image degradation/restoration process aims to recover and enhance image quality effectively.