Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Visual Communication and Image Representation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
