Image restoration is a long-standing low-level vision problem, eg, deblurring, deraining and
desnowing. In the process of image restoration, it is necessary to consider not only the …

Self-supervised learning methods have demonstrated promising benefits to feature
representation learning for image dehazing tasks, especially for avoiding the laborious work …

In the past decade, guided image filtering (GIF) has emerged as a successful edge-
preserving smoothing technique designed to remove noise while retaining important edges …

Image restoration seeks to obtain a high-quality image by eliminating degradations. While
existing methods have shown remarkable performance in image restoration, the majority are …

This paper presents a deep learning method for image dehazing and clarification. The main
advantages of the method are high computational speed and using unpaired image data for …

In construction scenes, the special presence of nonhomogeneous haze significantly affects
the performance of various visual perception tasks. To address this issue, this paper …

With rapid improvement of deep learning, significant progress has been made in image
dehazing, leading to favorable outcomes in many methods. However, a common challenge …

Owing to the heterogeneous spatial distribution and non-uniform morphological
characteristics of haze in remote sensing images (RSIs), conventional dehazing algorithms …

Images captured under haze conditions suffer from color distortion and low saturation due to
the light propagates through scattering particles, causing light intensity attenuation and …