1 citations · 1 across the 9 of their papers we have counts for
9 papers
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…
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…
Rethinking Diffusion Model-Based Video Super-Resolution: Leveraging Dense Guidance from Aligned Features
Jingyi Xu, Meisong Zheng, Ying Chen +3
Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. However, they suffer from error accumulation, spatial artifacts, and a tra…
NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
Nikolay Safonov, Alexey Bryncev, Andrey Moskalenko +28
This paper presents an overview of the NTIRE 2025 Challenge on UGC Video Enhancement. The challenge constructed a set of 150 user-generated content videos without reference ground…
SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network
Jun-Jie Huang, Zihan Chen, Tianrui Liu +5
Existing image steganography methods face fundamental limitations in hiding capacity (typically images) due to severe information interference and uncoordinated capacity-d…
CODA: Repurposing Continuous VAEs for Discrete Tokenization
Zeyu Liu, Zanlin Ni, Yeguo Hua +4
Discrete visual tokenizers transform images into a sequence of tokens, enabling token-based visual generation akin to language models. However, this process is inherently challengi…
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…
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…