Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Images captured under poor illumination conditions often exhibit characteristics such as low
brightness, low contrast, a narrow gray range, and color distortion, as well as considerable …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

Underwater images suffer from color casts and low contrast due to wavelength-and distance-
dependent attenuation and scattering. To solve these two degradation issues, we present an …

To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the
map** relationship between them is one-to-many. Previous works based on the pixel-wise …

This paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE),
which formulates light enhancement as a task of image-specific curve estimation with a deep …

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of
an image captured in an environment with poor illumination. Recent advances in this area …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …