DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor

• URL: http://arxiv.org/abs/2406.08377v3
• Date: Tue, 15 Oct 2024 03:17:14 GMT
• Title: DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
• Authors: Juncheng Wu, Zhangkai Ni, Hanli Wang, Wenhan Yang, Yuyin Zhou, Shiqi Wang,
• Abstract summary: We present Deep Degradation Response ( DDR), a method to quantify changes in image deep features under varying degradation conditions. Extensive evaluations demonstrate the versatility of DDR as an image descriptor, with strong correlations observed with key image attributes. DDR serves as an effective unsupervised learning objective in image restoration tasks, yielding notable advancements in image deblurring and single-image super-resolution.
• Score: 54.90762380689283
• License:
• Abstract: 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

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