  Single image inverse problem is a notoriously challenging ill-posed problem
that aims to restore the original image from one of its corrupted versions.
Recently, this field has been immensely influenced by the emergence of
deep-learning techniques. Deep Image Prior (DIP) offers a new approach that
forces the recovered image to be synthesized from a given deep architecture.
While DIP is quite an effective unsupervised approach, it is deprecated in
real-world applications because of the requirement of human assistance. In this
work, we aim to find the best-recovered image without the assistance of humans
by adding a stopping criterion, which will reach maximum when the iteration no
longer improves the image quality. More specifically, we propose to add a
pseudo noise to the corrupted image and measure the pseudo-noise component in
the recovered image by the orthogonality between signal and noise. The accuracy
of the orthogonal stopping criterion has been demonstrated for several tested
problems such as denoising, super-resolution, and inpainting, in which 38 out
of 40 experiments are higher than 95%.
