Image denoising is an important topic in the field of image processing. With the application of nonlocal similarity in sparse representation, the work of image denoising began to be performed on similar patch groups. The sparse representations of patches in a group will be learned together. In this paper, we propose a novel image denoising model by combining group sparsity residual with low-rankness. Firstly, motivated by the relationship between low rank and sparsity, a low rank constraint is imposed on the sparse coefficient matrix of each similar patch group to enhance the sparsity. Secondly, since 𝛾-norm can most closely match the true rank of a matrix, it is applied for rank approximation in our model. Finally, in view of the fact that numerous iterations are required in the group sparse representation (GSR) model, we develop an efficient algorithm based on the Majorize-Minimization (MM) optimization. It greatly reduces the computational complexity and the number of iterations. Experimental results show that our model makes great improvements in image denoising and outperforms many state-of-the-art methods.
Nowadays, medical image fusion serves as a significant aid for the precise diagnosis or surgical navigation. In this paper, we propose a novel tensor factorization based fusion strategy which well combines the multimodal, multiscale nature of medical images and multiway structure of tensors. Since our model adopts the sparse representation (SR) prior, we suffer from the systematic underestimation of the true solution because of the L1-norm regularization term. To address this problem, we introduce the generalized minimax-concave (GMC) penalty into our framework, which is a non-convex regularization term itself. It is beneficial for the whole cost function to maintain convexity. Furthermore, we combine the alternating direction method of multipliers (ADMM) algorithm and forward-backward (FB) method to achieve the optimization process. We conduct extensive experiments on five kinds of practical medical image fusion problems with 96 pairs of images in total. The results confirm that our model has great improvements in visual performance and objective metrics against the existing methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.