activity
20242026
most citedA Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction

15 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

Jiatong Li, Zheng Chen, Kai Liu +91

This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…

cs.CV2026

Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution

Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu +3

Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distil…

cs.CV2025

Learning Ordinal Degradation Representations with Textual Priors for Diffusion-Based Blind Image Super-Resolution

Yi-Cheng Liao, Shyang-En Weng, Yu-Syuan Xu +3

Blind image super-resolution (Blind SR) has achieved remarkable perceptual quality via generative priors. However, lacking clear degradation representations such as varying severit…

eess.IV2024★ 2 cited

Rethinking Theoretical Illumination for Efficient Low-Light Image Enhancement

Shyang-En Weng, Cheng-Yen Hsiao, Li-Wei Lu +4

Enhancing low-light images remains a critical challenge in computer vision, as does designing lightweight models for edge devices that can handle the computational demands of deep…

eess.IV2024★ 15 cited

A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction

Shyang-En Weng, Shaou-Gang Miaou, Ricky Christanto

Human vision relies heavily on available ambient light to perceive objects. Low-light scenes pose two distinct challenges: information loss due to insufficient illumination and und…