43 citations · 83 across the 9 of their papers we have counts for
11 papers
PIC-UIE: Predicting Image-Adaptive Corrections for Lightweight Underwater Image Enhancement
Cunhao Zhu, Dongliang Xu, Xiangtao Kong +3
Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenuation and backscatter. State…
DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement
Cunhao Zhu, Xiangtao Kong, Dongliang Xu +3
Underwater image enhancement is challenged by spatially non-uniform, wavelength-dependent attenuation. Propagation distance and wavelength govern this degradation, while YCbCr sepa…
A Preliminary Exploration Towards General Image Restoration
Xiangtao Kong, Jinjin Gu, Yihao Liu +4
Despite the tremendous success of deep models in various individual image restoration tasks, there are at least two major technical challenges preventing these works from being app…
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Fanghua Yu, Jinjin Gu, Zheyuan Li +6
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-mod…
Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy
Xiangtao Kong, Chao Dong, Lei Zhang
While single task image restoration (IR) has achieved significant successes, it remains a challenging issue to train a single model which can tackle multiple IR tasks. In this work…
HAT: Hybrid Attention Transformer for Image Restoration
Xiangyu Chen, Xintao Wang, Wenlong Zhang +4
Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only…
Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
Jinjin Gu, Xianzheng Ma, Xiangtao Kong +2
Deep deraining networks consistently encounter substantial generalization issues when deployed in real-world applications, although they are successful in laboratory benchmarks. A…
Reflash Dropout in Image Super-Resolution
Xiangtao Kong, Xina Liu, Jinjin Gu +2
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic re…
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
Xiangtao Kong, Hengyuan Zhao, Yu Qiao +1
We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this proc…
Efficient Image Super-Resolution Using Pixel Attention
Hengyuan Zhao, Xiangtao Kong, Jingwen He +2
This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective ne…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…