70 citations · 76 across the 18 of their papers we have counts for
18 papers
CoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors
Shengxi Li, Zhaokun Hu, Ce Zheng +3
Unsupervised conditional image generation (UCGen) aims to control generation without relying on manually annotated labels, yet remains challenging due to unstructured semantic repr…
Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration
Xinghua Huang, Zhixiong Yang, Chen Wu +6
A fundamental difficulty in all-in-one blind image restoration is that degradation is usually treated as an implicit factor hidden in degraded-to-clean mapping, rather than as an e…
Low Light Image Enhancement Challenge at NTIRE 2026
George Ciubotariu, Sharif S M A, Abdur Rehman +90
This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this cha…
NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results
Xin Li, Jiachao Gong, Xijun Wang +75
This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-…
REMAC: Reference-Based Martian Asymmetrical Image Compression
Qing Ding, Mai Xu, Shengxi Li +2
To expedite space exploration on Mars, it is indispensable to develop an efficient Martian image compression method for transmitting images through the constrained Mars-to-Earth co…
Benchmarking and Enhancing VLM for Compressed Image Understanding
Zifu Zhang, Tongda Xu, Siqi Li +4
With the rapid development of Vision-Language Models (VLMs) and the growing demand for their applications, efficient compression of the image inputs has become increasingly importa…
Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks
Xiaoye Liang, Lai Jiang, Minglang Qiao +6
In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, th…
Breaking the Multi-Enhancement Bottleneck: Domain-Consistent Quality Enhancement for Compressed Images
Qunliang Xing, Mai Xu, Jing Yang +1
Quality enhancement methods have been widely integrated into visual communication pipelines to mitigate artifacts in compressed images. Ideally, these quality enhancement methods s…
Hierarchical Semantic Compression for Consistent Image Semantic Restoration
Shengxi Li, Zifu Zhang, Mai Xu +3
The emerging semantic compression has been receiving increasing research efforts most recently, capable of achieving high fidelity restoration during compression, even at extremely…
Continuous Patch Stitching for Block-wise Image Compression
Zifu Zhang, Shengxi Li, Henan Liu +2
Most recently, learned image compression methods have outpaced traditional hand-crafted standard codecs. However, their inference typically requires to input the whole image at the…
InstructOCR: Instruction Boosting Scene Text Spotting
Chen Duan, Qianyi Jiang, Pei Fu +5
In the field of scene text spotting, previous OCR methods primarily relied on image encoders and pre-trained text information, but they often overlooked the advantages of incorpora…
Causal Context Adjustment Loss for Learned Image Compression
Minghao Han, Shiyin Jiang, Shengxi Li +4
In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniq…
QVD: Post-training Quantization for Video Diffusion Models
Shilong Tian, Hong Chen, Chengtao Lv +6
Recently, video diffusion models (VDMs) have garnered significant attention due to their notable advancements in generating coherent and realistic video content. However, processin…
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
Jingyuan Xia, Zhixiong Yang, Shengxi Li +4
Learning-based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors ar…
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
Zhixiong Yang, Jingyuan Xia, Shengxi Li +5
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pre-training on labell…
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
Qunliang Xing, Mai Xu, Shengxi Li +4
Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a…
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video
Li Yang, Mai Xu, Shengxi Li +2
Blind visual quality assessment (BVQA) on 360{\textdegree} video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360{\textdegree} video,…
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
Jingyuan Xia, Shengxi Li, Jun-Jie Huang +2
In this paper, we propose a novel solution for non-convex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that sp…