Machine Vision Quality Assessment for Image Restoration

In recent years, substantial progress has been made in the realm of No-Reference Image Quality Assessment (NR-IQA) for image restoration, where performance has been predominantly evaluated using metrics such as BRISQUE [1] and Hyper-IQA [2]. However, these NR-IQA metrics assess the perceptual quality of images without considering their utility in specific machine vision tasks, such as object detection and semantic segmentation. In this paper, we propose a Machine Vision Quality Assessment (MVQA) framework for image restoration. Specifically, we introduce the weighted Alternative Free-response Operating Characteristic (wAFROC) [3] as a metric to assess the machine vision quality of three image restoration sub-tasks: dehazing, denoising, and superresolution. By accounting for both detection sensitivity and spatial localization, wAFROC provides a more comprehensive evaluation of image quality in machine vision contexts. Its effectiveness is validated through downstream tasks, specifically object detection and semantic segmentation. We construct an IQA dataset for image restoration to explore the impact of various image restoration algorithms on the accuracy of object detection and semantic segmentation algorithms. Extensive experimental results demonstrate that the MVQA framework, leveraging wAFROC, effectively predicts the influence of image quality on machine vision tasks, bridging the gap between perceptual IQA and task-specific quality requirements in machine vision applications.