Recently, vision-based detection (VD) technology has been well-developed, and its general-
purpose object detection algorithms have been applied in various scenes. VD can be …

Recently, convolutional sparse coding (CSC) has shown great success in many image
processing tasks, such as image super-resolution and image separation. However, it …

The single image de-snowing task is an essential topic in computer vision, as images
captured on snowy days degrade the performance of current vision-based intelligent …

Color image restoration methods typically represent images as vectors in Euclidean space
or combinations of three monochrome channels. However, they often overlook the …

Image restoration aims to restore high-quality images from degraded counterparts and has
seen significant advancements through deep learning techniques. The technique has been …

This paper introduces a new hyperspectral image denoising method called Non-local
Convolutional Neural Network Denoiser (NL-CNND). The technique exploits data in four …

Residual learning is one of the most effective components in blind image denoising. It learns
to estimate the noise instead of the clean image itself. A shortcoming of residual learning is …

As multimedia content often contains noise from intrinsic defects of digital devices, image
denoising is an important step for high-level vision recognition tasks. Although several …

Abstract The Non-Local Means (NLM) algorithm is a fundamental denoising technique
widely utilized in various domains of image processing. However, further research is …

Sparse view computed tomography (CT) reconstruction poses a challenging ill-posed
inverse problem, necessitating effective regularization techniques. In this letter, we employ …