@inproceedings{
zhu2024perceptualdistortion,
title={Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem},
author={Qiwen Zhu and Yanjie Wang and Shilv Cai and Liqun Chen and Jiahuan Zhou and Luxin Yan and Sheng Zhong and Xu Zou},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=LgDMOie21k}
}
In this paper, we introduce a novel approach to single-image super-resolution (SISR) that balances perceptual quality and distortion through multi-objective optimization (MOO). Traditional pixel-based distortion metrics like PSNR and SSIM often fail to align with human perceptual quality, resulting in blurry outputs despite high scores. To address this, we propose the Multi-Objective Bayesian Optimization Super-Resolution (MOBOSR) framework, which dynamically adjusts loss weights during training. This reduces the need for manual hyperparameter tuning and lessens computational demands compared to AutoML. Our method conceptualizes the relationship between loss weights and image quality assessment (IQA) metrics as black-box objective functions, optimized to achieve an optimal perception-distortion Pareto frontier. Extensive experiments demonstrate that MOBOSR surpasses current state-of-the-art methods in both perception and distortion, significantly advancing the perception-distortion Pareto frontier. Our work lays a foundation for future exploration of the balance between perceptual quality and fidelity in image restoration tasks. Source codes and pretrained models are available at: https://github.com/ZhuKeven/MOBOSR.
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem
Qiwen Zhu Yanjie Wang Shilv Cai Liqun Chen Jiahuan Zhou Luxin Yan Sheng Zhong Xu Zou