A restoration algorithm for images contaminated by mixed Gaussian plus random-valued impulse noise

In this paper, we study the problem of restoring the image corrupted by additive Gaussian noise plus random-valued impulse noise. A novel noise classifier is firstly created to identify different noise in the corrupted image. Then, we use the remaining effective information to train an adaptive overcomplete dictionary for sparse representation of image patches with the help of masked K-SVD algorithm. Because of the adaptive nature of the learned dictionary, it can represent the image patches in concern more efficiently. Then, we minimize a variational model containing an optional data-fidelity term and a smooth regularization term respecting sparse representation of every image patch to get the final restored image. Extensive experimental results prove that our method cannot only remove noise from the corrupted image well, but also preserve more details and textures. It surpasses some state-of-the-art methods.

► A noise classifier to identify Gaussian and random-valued impulse noisy pixels.
► Adaptive overcomplete dictionary training based on the noisy image itself.
► A flexible variational model for restoring the image corrupted by mixed noise.