Recent deep learning methods have achieved promising results in image shadow removal.
However, their restored images still suffer from unsatisfactory boundary artifacts, due to the …

Previous raw image-based low-light image enhancement methods predominantly relied on
feed-forward neural networks to learn deterministic mappings from low-light to normally …

Recent deep-learning-based compression methods have achieved superior performance
compared with traditional approaches. However, deep learning models have proven to be …

Recent deep learning methods have achieved promising results in image shadow removal.
However, most of the existing approaches focus on working locally within shadow and non …

Abstract sRGB images are now the predominant choice for pre-training visual models in
computer vision research, owing to their ease of acquisition and efficient storage …

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB
counterparts obtained by camera ISPs. Learning the forward and inverse processes of …

Unprocessed RAW video has shown distinct advantages over sRGB video in video editing
and computer vision tasks. However capturing RAW video is challenging due to limitations …

Recent advancements in deep learning-based compression techniques have surpassed
traditional methods. However, deep neural networks remain vulnerable to backdoor attacks …

While raw images possess distinct advantages over sRGB images, eg, linearity and fine-
grained quantization levels, they are not widely adopted by general users due to their …

While RAW images are efficient for image editing and perception tasks, their large size can
strain camera storage and bandwidth. Reconstruction methods of RAW images from sRGB …