arXiv subjects

Shyang-En Weng

Publications and source records attributed to Shyang-En Weng.

5 recordsLinked to original sources

arXiv2026

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

Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distill it into a single step. General one-step methods fine-tune the generative prior into a deterministic mapping, restoring efficiency but discarding its stochastic nature. Conversely, recent attempts re-engage generation by shifting the timestep or injecting random noise, adjusting either the position or the state while the other stays fixed. Because only one side is controlled, the two align at isolated preset timesteps but drift apart once steered, leaving generation unstable. To address this, we present one-step diffusion via Inversion and Degradation-aware Sampling for Real-ISR (IDaS-SR), a one-step framework that bridges deterministic restoration and stochastic generation. At its core, Manifold Anchoring grounds the low-quality latent on the pretrained trajectory through two operations jointly estimated by the Manifold Inversion Noise Estimator (MINE): positioning declares where the latent lies and how it deviates from the clean state, while inversion aligns the latent to the declared position. Upon the anchor, CHARIOT reintroduces controlled stochasticity by jointly rescheduling the trajectory and interpolating the noise, enabling a single scalar to smoothly navigate the fidelity-realism trade-off. Extensive experiments demonstrate that IDaS-SR effectively unleashes the generative prior, achieving state-of-the-art performance under explicit control in a single inference step.

cs.CV↗

arXiv2026

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

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 aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through unknown degradations with a x4 scaling factor while ensuring the models remain executable on mobile devices. The objective is to develop effective and efficient network designs or solutions that achieve state-of-the-art real-world image super-resolution performance. The track of the challenge evaluates performance using a weighted combination of image quality assessment (IQA) score and speedup ratios. The competition attracted 108 registrants, with 16 teams achieving a valid score in the final ranking. This collaborative effort advances the performance of mobile real-world image super-resolution while offering an in-depth overview of the latest trends in the field.

cs.CV↗

arXiv2025

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

Blind image super-resolution (Blind SR) has achieved remarkable perceptual quality via generative priors. However, lacking clear degradation representations such as varying severity and mixtures, these methods fail to accurately reflect the actual degradation process. This limitation severely compromises restoration fidelity and leads to content inconsistencies, especially in diffusion-based blind SR models that rely on simple textual descriptions for contextual guidance. To bridge the gap between high-level semantics and low-level degradation artifacts, we introduce Ordinal Degradation CLIP (OD-CLIP), leveraging textual priors to enhance the learning of continuous degradation-level representations. Unlike standard CLIP text encoders, which struggle to represent numerical intensity, OD-CLIP moves beyond coarse labels by modeling unknown degradations as a continuous spectrum representing quality. By learning an ordinal embedding from low-quality inputs, our design captures both degradation types and their relative severity, explicitly modeling the degradation hierarchy and enabling interpolation across unseen levels. In our experiments, the OD-CLIP representation demonstrates stronger ordinal ranking and perceptual distance modeling compared to baseline methods. When applied to blind SR, we show that conditioning on OD-CLIP maintains fidelity and preserves content structures over existing methods in both unknown and mixed-degradation settings on real-world benchmarks.

cs.CV↗

arXiv2024

Rethinking Theoretical Illumination for Efficient Low-Light Image Enhancement

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 learning. This article introduces an extended version of the Channel-Prior and Gamma-Estimation Network (CPGA-Net), termed CPGA-Net+, incorporating the theoretically-based Attentions for illumination in local and global processing. Additionally, we assess our approach through a theoretical analysis of the block design by introducing both an ultra-lightweight and a stronger version, following the same design principles. The lightweight version significantly reduces computational costs by over two-thirds by utilizing the local branch as an auxiliary component. Meanwhile, the stronger version achieves an impressive balance by maximizing local and global processing capabilities. Our proposed methods have been validated as effective compared to recent lightweight approaches, offering superior performance and scalable solutions with limited computational resources.

eess.IV↗

arXiv2024

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

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 undesirable brightness shifts. Low-light image enhancement (LLIE) refers to image enhancement technology tailored to handle this scenario. We introduce CPGA-Net, an innovative LLIE network that combines dark/bright channel priors and gamma correction via deep learning and integrates features inspired by the Atmospheric Scattering Model and the Retinex Theory. This approach combines the use of traditional and deep learning methodologies, designed within a simple yet efficient architectural framework that focuses on essential feature extraction. The resulting CPGA-Net is a lightweight network with only 0.025 million parameters and 0.030 seconds for inference time, yet it achieves superior performance over existing LLIE methods on both objective and subjective evaluation criteria. Furthermore, we utilized knowledge distillation with explainable factors and proposed an efficient version that achieves 0.018 million parameters and 0.006 seconds for inference time. The proposed approaches inject new solution ideas into LLIE, providing practical applications in challenging low-light scenarios.

eess.IV↗