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Learning Implicit Neural Degradation Representation for Unpaired Image Dehazing
Shuaibin Fan, Senming Zhong, Wenchao Yan +1
Image dehazing is an important task in the field of computer vision, aiming at restoring clear and detail-rich visual content from haze-affected images. However, when dealing with…
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…
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…
Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
Shuaibin Fan, Minglong Xue, Aoxiang Ning +1
Although synthetic data can alleviate acquisition challenges in image dehazing tasks, it also introduces the problem of domain bias when dealing with small-scale data. This paper p…
Artistic-style text detector and a new Movie-Poster dataset
Aoxiang Ning, Yiting Wei, Minglong Xue +1
Although current text detection algorithms demonstrate effectiveness in general scenarios, their performance declines when confronted with artistic-style text featuring complex str…