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

We show that raw degradation features can effectively guide deep restoration models,
providing accurate degradation priors to facilitate better restoration. While networks that do …

Real-world low-light images suffer from two main degradations, namely, inevitable noise
and poor visibility. Since the noise exhibits different levels, its estimation has been …

Rainy weather is a challenge for many vision-oriented tasks (eg, object detection and
segmentation), which causes performance degradation. Image deraining is an effective …

Recovering noise-covered details from low-light images is challenging, and the results given
by previous methods leave room for improvement. Recent diffusion models show realistic …

Lightness adaptation is vital to the success of image processing to avoid unexpected visual
deterioration, which covers multiple aspects, eg, low-light image enhancement, image …

Most consumer-grade digital cameras can only capture a limited range of luminance in real-
world scenes due to sensor constraints. Besides, noise and quantization errors are often …

Abstract The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image
enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it …

Nowadays modern displays are capable to render video content with high dynamic range
(HDR) and wide color gamut (WCG). However, most available resources are still in standard …

Underexposed image enhancement is of importance in many research domains. In this
paper, we take this problem as image feature transformation between the underexposed …