A Critical Analysis of Patch Similarity Based Image Denoising Algorithms
- URL: http://arxiv.org/abs/2008.10824v1
- Date: Tue, 25 Aug 2020 05:30:37 GMT
- Title: A Critical Analysis of Patch Similarity Based Image Denoising Algorithms
- Authors: Varuna De Silva
- Abstract summary: Image denoising is a classical signal processing problem.
Most of the algorithms for image denoising has focused on the paradigm of non-local similarity.
This paper reviews multiple aspects of image denoising algorithm development based on non-local similarity.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image denoising is a classical signal processing problem that has received
significant interest within the image processing community during the past two
decades. Most of the algorithms for image denoising has focused on the paradigm
of non-local similarity, where image blocks in the neighborhood that are
similar, are collected to build a basis for reconstruction. Through rigorous
experimentation, this paper reviews multiple aspects of image denoising
algorithm development based on non-local similarity. Firstly, the concept of
non-local similarity as a foundational quality that exists in natural images
has not received adequate attention. Secondly, the image denoising algorithms
that are developed are a combination of multiple building blocks, making
comparison among them a tedious task. Finally, most of the work surrounding
image denoising presents performance results based on Peak-Signal-to-Noise
Ratio (PSNR) between a denoised image and a reference image (which is perturbed
with Additive White Gaussian Noise). This paper starts with a statistical
analysis on non-local similarity and its effectiveness under various noise
levels, followed by a theoretical comparison of different state-of-the-art
image denoising algorithms. Finally, we argue for a methodological overhaul to
incorporate no-reference image quality measures and unprocessed images (raw)
during performance evaluation of image denoising algorithms.
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