Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity

Abstract

This paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.

Pixel Analysis Technique for Image Denoising

“…Ruiqin Xiong, et.al (2016) proposed in this paper [9], another picture denoising algorithm in view of adaptive signal modeling and regularization. It improves the quality of pictures by regularizing each picture patch utilizing bandwise distribution modeling in transform domain.…”

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

“…In [36] they filtered an image in wavelet domain, in which the coefficients at adjacent positions and scales were firstly modeled as a Gaussian scale mixture model, then the wavelet coefficients were updated by Bayesian least-squares estimation. Besides, some methods incorporate nonlocal similarity into the transform-domain method [37,38,39]. For example, Ref.…”

“…[39] proposed a block-matching and 3-D filtering (BM3D) method, which is based on [36]. The authors of [37] regularized each image patch by band-wise distribution modeling in transform domain. In [38], stripe noise is first separated using wavelet-Fourier filter, then remaining random noise is removed using the multiscale NLM filter.…”

Non-linear adaptive image enhancement in wireless sensor networks based on non-subsampled shearlet transform

“…The visibility and contrast of the captured images may be affected and may be degraded by many reasons, such as poor environmental condition, camera sensor noise, and other uncertain factors [3,4]. It is a necessary step before further processing and understanding the images to improve image quality with enhancement and denoising algorithm in many vision applications [5,6]. The contrast and detail of images can be improved remarkably by those algorithms, so it will be easier for human or machine to identify and understand.…”