20 citations · 25 across the 7 of their papers we have counts for
9 papers
Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution
Yu-Syuan Xu, Hao-Lun Sun, Hao-Wei Chen +2
Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscaling factors. While traditional…
Parallel Sampling of Diffusion Models on
Yan-Ting Chen, Hao-Wei Chen, Tsu-Ching Hsiao +1
In this paper, we design an algorithm to accelerate the diffusion process on the manifold. The inherently sequential nature of diffusion models necessitates substantial tim…
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Yu-Cheng Lin, Yu-Syuan Xu, Hao-Wei Chen +2
Image restoration is a key task in low-level computer vision that aims to reconstruct high-quality images from degraded inputs. The emergence of Vision Mamba, which draws inspirati…
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung +4
Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noi…
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters
Hao-Wei Chen, Yu-Syuan Xu, Kelvin C. K. Chan +3
Existing image restoration approaches typically employ extensive networks specifically trained for designated degradations. Despite being effective, such methods inevitably entail…
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang +4
Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during ima…
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Yuki Kondo, Norimichi Ukita, Takayuki Yamaguchi +19
Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a ch…
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong +3
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT…
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo +3
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with…