Towards the Automation of Deep Image Prior
This work addresses the need for human-free automation in DIP applications, making it more practical for real-world use, though it is incremental as it builds on existing DIP methods.
The paper tackles the challenge of automating Deep Image Prior (DIP) for single image inverse problems by introducing a stopping criterion based on pseudo-noise orthogonality, achieving over 95% accuracy in 38 out of 40 experiments across tasks like denoising and super-resolution.
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%.