Abstract
Non-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.
Highlights
Image denoising is one of the most fundamental and important tasks in image processing and computer vision
Regarding the way to separate uori from u, the denoising methods can be divided into two classes: those implemented in the spatial domain and those implemented in the transform domain
This paper aims at developing the non-local means (NLMs) by introducing the graph signal processing theory and a multi-layer framework
Summary
Image denoising is one of the most fundamental and important tasks in image processing and computer vision Speaking, it aims at retrieving the clean image uori ∈ RS1×S2 from an observed noisy image u ∈ RS1×S2. Various extensions have been proposed to balance the smoothness and details, e.g., averaging in local windows with adaptive size [22] or local regions with adaptive shape [32]. In contrast to these connected local regions, Buades et al proposed a method to average pixels in non-local regions named non-local means (NLMs) [3]. The main idea of NLMs is to select similar pixels
Sign-in/Register to view highlights & summary 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 callDisclaimer: 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.
