Image Restoration and Enhancement

Image restoration estimates a cleaner image using information about the degradation process. Image enhancement, by contrast, usually aims to improve visibility or perceived quality.

Noise and blur

Gaussian, impulse and Poisson-like noise arise from different physical mechanisms. Median filtering works well for impulse noise, while other distributions call for different estimators.

Blur can result from defocus, camera motion or object motion. Wiener filtering and deconvolution methods estimate sharper content when the degradation function is sufficiently known. Blind deconvolution estimates both the image and the blur model.

Contrast and missing regions

Histogram equalisation, gamma correction and local-contrast methods can expose low-contrast structure. Inpainting fills missing regions from surrounding structure or learned priors.

Details generated by a learned completion model are not direct measurements from the source image. Preserving the original recording separately from derived versions is therefore important in measurement and forensic workflows.

Super-resolution

Super-resolution estimates a higher-resolution representation from one or more lower-resolution observations. Pixel-oriented objectives usually produce conservative outputs, while perceptual or adversarial objectives may create sharper but more synthetic detail.

The value of restoration depends not only on appearance but also on whether measured information can be distinguished from estimated information.