10 citations · 12 across the 7 of their papers we have counts for
7 papers
Degradation-Consistent Learning via Bidirectional Diffusion for Low-Light Image Enhancement
Jinhong He, Minglong Xue, Zhipu Liu +3
Low-light image enhancement aims to improve the visibility of degraded images to better align with human visual perception. While diffusion-based methods have shown promising perfo…
Unified Image Restoration and Enhancement: Degradation Calibrated Cycle Reconstruction Diffusion Model
Minglong Xue, Jinhong He, Shivakumara Palaiahnakote +1
Image restoration and enhancement are pivotal for numerous computer vision applications, yet unifying these tasks efficiently remains a significant challenge. Inspired by the itera…
Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion
Jinhong He, Shivakumara Palaiahnakote, Aoxiang Ning +1
Due to the singularity of real-world paired datasets and the complexity of low-light environments, this leads to supervised methods lacking a degree of scene generalisation. Meanwh…
Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori
Jinhong He, Minglong Xue, Wenhai Wang +1
Low-light video enhancement is highly demanding in maintaining spatiotemporal color consistency. Therefore, improving the accuracy of color mapping and keeping the latency low is c…
KAN See In the Dark
Aoxiang Ning, Minglong Xue, Jinhong He +1
Existing low-light image enhancement methods are difficult to fit the complex nonlinear relationship between normal and low-light images due to uneven illumination and noise effect…
Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement
Jinhong He, Minglong Xue, Aoxiang Ning +1
Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods l…
Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion
Minglong Xue, Jinhong He, Wenhai Wang +1
Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual perception are still significant challenges. To…