Perceptual Fairness in Image Restoration
Guy Ohayon, Michael Elad, Tomer Michaeli

TL;DR
This paper introduces a new fairness measure for image restoration called Group Perceptual Index (GPI), which compares the distribution of reconstructed images to ground truth, aiming for equal perceptual quality across groups.
Contribution
It proposes a novel, distribution-based fairness metric (GPI) for image restoration and demonstrates its application and advantages over existing definitions.
Findings
GPI effectively measures perceptual fairness across groups.
State-of-the-art algorithms can be evaluated for fairness using GPI.
Theoretical analysis links GPI to previous fairness notions.
Abstract
Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (e.g., women) only if it falls within the group's set of ground truth images (e.g., natural images of women); otherwise, it is considered entirely incorrect. Consequently, such definitions are prone to controversy, as errors in image restoration can manifest in various ways. In this work we offer an alternative approach towards fairness in image restoration, by considering the Group Perceptual Index (GPI), which we define as the statistical distance between the distribution of the group's ground truth images and the distribution of their reconstructions. We assess the fairness of an algorithm by comparing the GPI of different groups, and say…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Video Quality Assessment · Advanced Image Fusion Techniques
MethodsSparse Evolutionary Training
