Existing low-light image enhancement techniques are mostly not only difficult to deal with
both visual quality and computational efficiency but also commonly invalid in unknown …

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 …

Deep neural networks have achieved remarkable progress in enhancing low-light images
by improving their brightness and eliminating noise. However, most existing methods …

Multi-view clustering on traditional optimization methods is derived from different theoretical
frameworks, yet it may be inefficient in dealing with complex multi-view data compared to …

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially
for enhancing visual quality and/or extracting aggregated features for perception. However …

Ultra-high-definition (UHD) image enhancement is a challenging problem that aims to
effectively and efficiently recover clean UHD images. To maintain efficiency, the …

While recent low-light image enhancement (LLIE) methods have made significant
advancements, they still face challenges in terms of low visual quality and weak …

One-shot medical image segmentation (MIS) aims to cope with the expensive time-
consuming and inherent human bias annotations. One prevalent method to address one …

Visual perception in low-light conditions (eg, nighttime) plays an important role in various
multimedia-related applications (eg, autonomous driving). The enhancement (provides a …

Object detection in low-light scenarios has attracted much attention in the past few years. A
mainstream and representative scheme introduces enhancers as the pre-processing for …