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Single image inverse problem is a notoriously challenging ill-posed problemthat aims to restore the original image from one of its corrupted versions.Recently, this field has been immensely influenced by the emergence ofdeep-learning techniques. Deep Image Prior (DIP) offers a new approach thatforces the recovered image to be synthesized from a given deep architecture.While DIP is quite an effective unsupervised approach, it is deprecated inreal-world applications because of the requirement of human assistance. In thiswork, we aim to find the best-recovered image without the assistance of humansby adding a stopping criterion, which will reach maximum when the iteration nolonger improves the image quality. More specifically, we propose to add apseudo noise to the corrupted image and measure the pseudo-noise component inthe recovered image by the orthogonality between signal and noise. The accuracyof the orthogonal stopping criterion has been demonstrated for several testedproblems such as denoising, super-resolution, and inpainting, in which 38 outof 40 experiments are higher than 95%.

Towards the Automation of Deep Image Prior