168 citations · 1.8k across the 114 of their papers we have counts for
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Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey
Xin Li, Yulin Ren, Xin Jin +5
Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various…
NVTC: Nonlinear Vector Transform Coding
Runsen Feng, Zongyu Guo, Weiping Li +1
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance. Recent state-of-the-art methods for neural image c…
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression
Yixin Gao, Runsen Feng, Zongyu Guo +1
Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significan…
Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts
Ruoyu Feng, Jinming Liu, Xin Jin +3
Image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information red…
Compression with Bayesian Implicit Neural Representations
Zongyu Guo, Gergely Flamich, Jiajun He +2
Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view,…
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Ruoyu Feng, Yixin Gao, Xin Jin +2
Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applicatio…
Inpaint Anything: Segment Anything Meets Image Inpainting
Tao Yu, Runseng Feng, Ruoyu Feng +4
Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first at…
Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective
Xin Li, Bingchen Li, Xin Jin +2
In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to…
Versatile Neural Processes for Learning Implicit Neural Representations
Zongyu Guo, Cuiling Lan, Zhizheng Zhang +2
Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Ne…