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 …

Low-light image enhancement is a key prerequisite for diverse applications in the field of
image processing and computer vision. Various approaches for this task have been …

The exclusive properties of RAW data have shown great potential for low-light image
enhancement. Nevertheless, the performance is bottlenecked by the inherent limitations of …

A practical low-light enhancement solution must be computationally fast, memory-efficient,
and achieve a visually appealing restoration. Most of the existing methods target restoration …

Images captured from low-light scenes often suffer from severe degradations, including low
visibility, color casts, intensive noises, etc. These factors not only degrade image qualities …

In this work, we propose a generative model for enhancement of images captured in low-
light conditions. Sensor constraints and inappropriate lighting conditions are accountable for …

Object detection in low-light conditions remains a challenging but important problem with
many practical implications. Some recent works show that, in low-light conditions, object …

Restoring high quality images from raw data in low light is challenging due to various noises
caused by limited photon count and complicated Image Signal Process (ISP). Although …

Color conveys important information about the visible world. However, under low-light
conditions, both pixel intensity, as well as true color distribution, can be significantly shifted …

Supervised networks address the task of low-light enhancement using paired images.
However, collecting a wide variety of low-light/clean paired images is tedious as the scene …