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 …

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

The extremes of lighting (eg too much or too little light) usually cause many troubles for
machine and human vision. Many recent works have mainly focused on under-exposure …

Underexposed images inevitably suffer severe degradation due to light distortion and noise
corruption. Motivated by the limited samples of paired datasets, several unsupervised …

Low-light images suffer severe degradation of low lightness and noise corruption, causing
unsatisfactory visual quality and visual recognition performance. To solve this problem while …

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

The following three factors restrict the application of existing low-light image enhancement
methods: unpredictable brightness degradation and noise, inherent gap between metric …

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 …

Images captured in low-light conditions often suffer from significant quality degradation.
Recent works have built a large variety of deep Retinex-based networks to enhance low …

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 …