Image Dehazing is a fast growing research area with several practical applications.
Dehazing improves the image quality that has been affected due to the scattering …

Single image dehazing is a critical problem in computer vision. However, most recently
proposed learning-based dehazing methods achieve unsatisfactory quality with dehazed …

The natural and artificial dispersal of climatic particles transforms images obtained in open-
air conditions. Due to visibility diminishing aerosols, unfavorable climate situations such as …

Deep convolutional neural networks (CNNs) have shown their advantages in the single
image de-raining task. However, most existing CNNs-based methods utilize only local …

Image dedusting has gained increasing attention as a preprocessing step for computer
vision tasks. Current traditional image dedusting methods rely on a variety of constraints or …

Low visibility in hazy weather causes the loss of image details in digital images captured by
some imaging devices such as monitors. This paper proposes an end-to-end U-Net based …

Convolutional neural networks (CNNs) based on the discriminative learning model have
been widely used for image denoising. In this study, a feed‐forward denoising CNN …

Removing haze, especially non-homogeneous and in various concentrations, is quite
challenging. Existing dehazing methods are usually used to deal with homogeneous haze …

Dust degrades image content and causes image color cast, which negatively impacts on
many high-level computer vision tasks. In this paper, we proposed a dedusting network with …

Hazy images often have color distortion, blur and other visible visual quality degradation,
affecting the performance of some advanced visual tasks. Therefore, single image dehazing …