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 …

Modern handheld devices can acquire burst image sequence in a quick succession.
However, the individual acquired frames suffer from multiple degradations and are …

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 …

Low-light image enhancement, a pervasive but challenging problem, plays a central role in
enhancing the visibility of an image captured in a poor illumination environment. Due to the …

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 …

Capturing images is a key part of automation for high-level tasks such as scene text
recognition. Low-light conditions pose a challenge for high-level perception stacks, which …

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 …

Burst image processing is becoming increasingly popular in recent years. However, it is a
challenging task since individual burst images undergo multiple degradations and often …

On a shutter press, modern handheld cameras capture multiple images in rapid succession
and merge them to gen-erate a single image. However, individual frames in a burst are …