Rendition: Reclaiming what a black box takes away
Peyman Milanfar

TL;DR
This paper introduces a simple, effective algorithm for reversing the effects of a black box image transformation without knowing its internal workings, applicable to various distortions.
Contribution
It proposes a novel, general approach to image rendition that works across different black box operators without requiring their explicit models.
Findings
The algorithm reliably reverses a wide class of image distortions.
It works for both contractive and expansive black box operators.
The method is simple and broadly applicable.
Abstract
The premise of our work is deceptively familiar: A black box has altered an image . Recover the image . This black box might be any number of simple or complicated things: a linear or non-linear filter, some app on your phone, etc. The latter is a good canonical example for the problem we address: Given only "the app" and an image produced by the app, find the image that was fed to the app. You can run the given image (or any other image) through the app as many times as you like, but you can not look inside the (code for the) app to see how it works. At first blush, the problem sounds a lot like a standard inverse problem, but it is not in the following sense: While we have access to the black box and can run any image through it and observe the output, we do not know how the block box alters the image. Therefore…
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