6 papers · 1 filter
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…
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…
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…