Images captured under incorrect exposures unavoidably suffer from mixed degradations of
lightness and structures. Most existing deep learning-based exposure correction methods …

This paper presents a new neural network for enhancing underexposed photos. Instead of
directly learning an image-to-image mapping as previous work, we introduce intermediate …

Low-light images typically suffer from two problems. First, they have low visibility (ie, small
pixel values). Second, noise becomes significant and disrupts the image content, due to low …

Enhancing images in low-light scenes is a challenging but widely concerned task in the
computer vision. The mainstream learning-based methods mainly acquire the enhanced …

Existing image enhancement methods are typically designed to address either the over-or
under-exposure problem in the input image. When the illumination of the input image …

Low-light image enhancement is important for high-quality image display and other visual
applications. However, it is a challenging task as the enhancement is expected to improve …

Underexposed images often suffer from serious quality degradation such as poor visibility
and latent noise in the dark. Most previous methods for underexposed images restoration …

Capturing photographs with wrong exposures remains a major source of errors in camera-
based imaging. Exposure problems are categorized as either:(i) overexposed, where the …

Enhancing the quality of low-light (LOL) images plays a very important role in many image
processing and multimedia applications. In recent years, a variety of deep learning …

Images captured with improper exposures usually bring unsatisfactory visual effects.
Previous works mainly focus on either underexposure or overexposure correction, resulting …