Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying degradation conditions. Specifically, our approach facilitates flexible and adaptive degradation, enabling the controlled synthesis of image degradation through text-driven prompts. Extensive evaluations demonstrate the versatility of DDR as an image descriptor, with strong correlations observed with key image attributes such as complexity, colorfulness, sharpness, and overall quality. Moreover, we demonstrate the efficacy of DDR across a spectrum of applications. It excels as a blind image quality assessment metric, outperforming existing methodologies across multiple datasets. Additionally, DDR serves as an effective unsupervised learning objective in image restoration tasks, yielding notable advancements in image deblurring and single-image super-resolution. Our code is available at: https://github.com/eezkni/DDR
@article{arxiv.2406.08377,
title = {DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor},
author = {Juncheng Wu and Zhangkai Ni and Hanli Wang and Wenhan Yang and Yuyin Zhou and Shiqi Wang},
journal= {arXiv preprint arXiv:2406.08377},
year = {2024}
}
Comments
Accepted to Advances in Neural Information Processing Systems (NeurIPS) 2024