Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …

Recently, diffusion models have been used to solve various inverse problems in an
unsupervised manner with appropriate modifications to the sampling process. However, the …

The inversion of real images into StyleGAN's latent space is a well-studied problem.
Nevertheless, applying existing approaches to real-world scenarios remains an open …

Recently, numerous facial editing techniques have been proposed that leverage the
generative power of a pretrained StyleGAN. To successfully edit an image this way, one …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …

Learning a good image prior is a long-term goal for image restoration and manipulation.
While existing methods like deep image prior (DIP) capture low-level image statistics, there …

We present AutoDIR, an innovative all-in-one image restoration system incorporating latent
diffusion. AutoDIR excels in its ability to automatically identify and restore images suffering …

Convolutional Neural Networks (CNNs) are highly effective for image reconstruction
problems. Typically, CNNs are trained on large amounts of training images. Recently …