4 papers
MiM-DiT: MoE in MoE with Diffusion Transformers for All-in-One Image Restoration
Lingshun Kong, Jiawei Zhang, Zhengpeng Duan +6
All-in-one image restoration is challenging because different degradation types, such as haze, blur, noise, and low-light, impose diverse requirements on restoration strategies, ma…
AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results
George Ciubotariu, Florin-Alexandru Vasluianu, Zhuyun Zhou +44
This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of…
Efficient Visual State Space Model for Image Deblurring
Lingshun Kong, Jiangxin Dong, Jinhui Tang +2
Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. While ViTs generally outperform CNNs by effectively ca…
DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models
Lingshun Kong, Jiawei Zhang, Dongqing Zou +4
Diffusion models have achieved significant progress in image generation. The pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image prio…