Abstract
Patch-based low rank matrix approximation has shown great potential in image denoising. Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising performance. For each local patch in an image, the WNNM method groups nonlocal similar patches by block matching to formulate a low-rank matrix. However, the WNNM often chooses irrelevant patches such that it may lose fine details of the image, resulting in undesirable artifacts in the final reconstruction. In this regards, this paper aims to provide a denoising algorithm which further improves the performance of the WNNM method. For this purpose, we develop a new nonlocal similarity measure by exploiting both pixel intensities and gradients and present a filter that enhances edge information in a patch to improve the performance of low rank approximation. The experimental results on widely used test images demonstrate that the proposed denoising algorithm performs better than other state-of-the-art denoising algorithms in terms of PSNR and SSIM indices as well as visual quality.
Highlights
Image denoising is one of the most fundamental problems in image processing
In order to circumvent this weakness of the weighted nuclear norm minimization (WNNM) algorithm, we propose an improved version of denoising algorithm
In this paper, we propose an image denoising algorithm based on the low rank matrix approximation
Summary
Image denoising is one of the most fundamental problems in image processing. It aims to recover the original (clean) image from the corrupted observation and is often used as a preprocessing step to high level computer vision applications such as segmentation and interpolation. State-of-theart image denoising methods include spatial domain based approaches such as total variation (TV) minimization [1], [2], kernel-based convolution [3], [4], and patch-based nonlocal means (NLM) [5]. By taking advantage of the spatial redundancy of patches occurring in an image, the nonlocal patch similarities are taken into account to perform weighted average of pixels. It is often easy to separate noise and signal, so that it can yield improved denoising results. This combination together with collaborative filtering greatly improves the denoising performance as verified by a state-of-the-art denoising algorithm, block-matching and 3D filtering (BM3D) [6]. The low rank matrix approximation has been successfully exploited in another cutting-edge method for the image restoration, called
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.
