10 citations · 10 across the 6 of their papers we have counts for
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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…
DFDNet: Dynamic Frequency-Guided De-Flare Network
Minglong Xue, Aoxiang Ning, Shivakumara Palaiahnakote +1
Strong light sources in nighttime photography frequently produce flares in images, significantly degrading visual quality and impacting the performance of downstream tasks. While s…
UR2P-Dehaze: Learning a Simple Image Dehaze Enhancer via Unpaired Rich Physical Prior
Minglong Xue, Shuaibin Fan, Shivakumara Palaiahnakote +1
Image dehazing techniques aim to enhance contrast and restore details, which are essential for preserving visual information and improving image processing accuracy. Existing metho…
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…
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…
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…