Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

Convolutional neural networks (CNNs) have shown dramatic improvements in single image
super-resolution (SISR) by using large-scale external samples. Despite their remarkable …

Group sparse representation (GSR) has made great strides in image restoration producing
superior performance, realized through employing a powerful mechanism to integrate the …

In the recent era, security of the data and information is a very big challenge. The society is
also facing a lot of challenges to preserve the security of information. The proposed research …

In this paper, we propose a novel approach to the rank minimization problem, termed rank
residual constraint (RRC) model. Different from existing low-rank based approaches, such …

In agriculture, identifying the deficient or excess nutrients in the leaf is a significant concern
for attaining the higher yield and productivity of any crop. Several image processing …

A novel image sequence denoising algorithm is presented. The proposed approach takes
advantage of the self-similarity and redundancy of adjacent frames. The algorithm is inspired …

Light field images taken by plenoptic cameras often have a tradeoff between spatial and
angular resolutions. In this paper, we propose a novel spatial super-resolution approach for …

Satellite image denoising is essential for enhancing the visual quality of images and for
facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite …