In this paper, we rethink the low-light image enhancement task and propose a physically
explainable and generative diffusion model for low-light image enhancement, termed as Diff …

To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the
map** relationship between them is one-to-many. Previous works based on the pixel-wise …

Night images suffer not only from low light, but also from uneven distributions of light. Most
existing night visibility enhancement methods focus mainly on enhancing low-light regions …

Due to the absence of a desirable objective for low-light image enhancement, previous data-
driven methods may provide undesirable enhanced results including amplified noise …

Under-exposure introduces a series of visual degradation, ie decreased visibility, intensive
noise, and biased color, etc. To address these problems, we propose a novel semi …

Deep learning-based methods have achieved remarkable success in image restoration and
enhancement, but are they still competitive when there is a lack of paired training data? As …

Low-light images suffer from low contrast and unclear details, which not only reduces the
available information for humans but limits the application of computer vision algorithms …

Retinex model is an effective tool for low-light image enhancement. It assumes that
observed images can be decomposed into the reflectance and illumination. Most existing …

Images captured in weak illumination conditions could seriously degrade the image quality.
Solving a series of degradation of low-light images can effectively improve the visual quality …

In this paper, we present a systematic review and evaluation of existing single-image low-
light enhancement algorithms. Besides the commonly used low-level vision oriented …