7 citations · 10 across the 6 of their papers we have counts for
6 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…
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…
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…
Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring
Lingshun Kong, Jiangxin Dong, Mingqiang Li +2
We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the…
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Andrey Ignatov, Radu Timofte, Maurizio Denna +93
Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions hav…