Images captured under poor illumination conditions often exhibit characteristics such as low
brightness, low contrast, a narrow gray range, and color distortion, as well as considerable …

Low-light image (LLI) enhancement is an important image processing task that aims at
improving the illumination of images taken under low-light conditions. Recently, a …

When enhancing low-light images, many deep learning algorithms are based on the Retinex
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …

As the quality of optical sensors improves, there is a need for processing large-scale
images. In particular, the ability of devices to capture ultra-high definition (UHD) images and …

Abstract Low-light Image Enhancement (LIE) aims at improving contrast and restoring
details for images captured in low-light conditions. Most of the previous LIE algorithms adjust …

Retinex model-based methods have shown to be effective in layer-wise manipulation with
well-designed priors for low-light image enhancement. However, the commonly used hand …

Diffusion models have achieved promising results in image restoration tasks, yet suffer from
time-consuming, excessive computational resource consumption, and unstable restoration …

This paper presents a new solution for low-light image enhancement by collectively
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …

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 …

We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-
LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel …