Background Digital images are captured using sensors during the data acquisition phase,
where they are often contaminated by noise (an undesired random signal). Such noise can …

We present a deep learning based method for low-light image enhancement. This problem
is challenging due to the difficulty in handling various factors simultaneously including …

Low-light is an inescapable element of our daily surroundings that greatly affects the
efficiency of our vision. Research works on low-light imagery have seen a steady growth …

Video surveillance systems have wide range of utilities, yet easily suffer from great quality
degeneration under dim light circumstances. Industrial solutions mainly use extra near …

Recently, deep-learning-based low-light video enhancement methods have drawn wide
attention and achieved remarkable performance. However, limited by the difficulty in …

Low-illumination images are usually taken in non-uniform environmental light, such as
extremely dark or bright light or artificial light. The enhancement results achieved by existing …

Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast
low-light imaging on commodity devices (eg. smartphone cameras) without the need for a …

Acquiring visually pleasing videos under insufficient lighting has been an important and
challenging task for both photographers and algorithm engineers. Current methods have …

Low-light images are seriously corrupted by noise due to the low signal-to-noise ratio. In low
intensity, just-noticeable-difference (JND) is high, and thus the noise is not perceived well by …

A noisy low-light image enhancement algorithm based on structure-texture-noise (STN)
decomposition is proposed in this work. We split an input image into structure, texture, and …