Publications (13)
Rethinking Blur Synthesis for Deep Real-World Image Deblurring
Hao Wei, Chenyang Ge, Xin Qiao +1
In this paper, we examine the problem of real-world image deblurring and take into account two key factors for improving the performance of the deep image deblurring model, namely,…
Geometric Transformation-Embedded Mamba for Learned Video Compression
Hao Wei, Yanhui Zhou, Chenyang Ge
Although learned video compression methods have exhibited outstanding performance, most of them typically follow a hybrid coding paradigm that requires explicit motion estimation a…
SAMIC: A Lightweight Semantic-Aware Mamba for Efficient Perceptual Image Compression
Jiaqian Zhang, Hao Wei, Chenyang Ge +1
Perceptual image compression focuses on preserving high visual quality under low-bitrate constraints. Most existing approaches to perceptual compression leverage the strong generat…
Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors
Hao Wei, Yanhui Zhou, Chenyang Ge +2
Most recent extreme rescaling methods struggle to preserve semantically consistent structures and produce realistic details, due to the severely ill-posed nature of low- to high-re…
Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
Hao Wei, Yanhui Zhou, Chenyang Ge +2
Most existing extreme compression methods fail to achieve an optimal rate-distortion-perception trade-off, as they typically prioritize perceptual fidelity and visual realism over…
A Lightweight Model for Perceptual Image Compression via Implicit Priors
Hao Wei, Yanhui Zhou, Yiwen Jia +3
Perceptual image compression has shown strong potential for producing visually appealing results at low bitrates, surpassing classical standards and pixel-wise distortion-oriented…
RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion
Zhiyuan Li, Yanhui Zhou, Hao Wei +2
Diffusion-based extreme image compression methods have achieved impressive performance at extremely low bitrates. However, constrained by the iterative denoising process that start…
Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior
Zhiyuan Li, Yanhui Zhou, Hao Wei +2
Image compression at extremely low bitrates (below 0.1 bits per pixel (bpp)) is a significant challenge due to substantial information loss. In this work, we propose a novel two-st…
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
Marcos V. Conde, Zhijun Lei, Wen Li +72
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolutio…
One-Step Diffusion for Perceptual Image Compression
Yiwen Jia, Hao Wei, Yanhui Zhou +1
Diffusion-based image compression methods have achieved notable progress, delivering high perceptual quality at low bitrates. However, their practical deployment is hindered by sig…
Traditional Transformation Theory Guided Model for Learned Image Compression
Zhiyuan Li, Chenyang Ge, Shun Li
Recently, many deep image compression methods have been proposed and achieved remarkable performance. However, these methods are dedicated to optimizing the compression performance…
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods
Xin Qiao, Matteo Poggi, Pengchao Deng +3
Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. The RGB guided ToF imaging system is crucial to several applic…
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
Xin Qiao, Chenyang Ge, Youmin Zhang +4
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The f…