Deep neural networks provide excellent performance for inverse problems such as
denoising. However, neural networks can be sensitive to adversarial or worst-case …

Following their success in visual recognition tasks, Vision Transformers (ViTs) are being
increasingly employed for image restoration. As a few recent works claim that ViTs for image …

Stochastic restoration algorithms allow to explore the space of solutions that correspond to
the degraded input. In this paper we reveal additional fundamental advantages of stochastic …

Image denoising is a fundamental task in computer vision. While prevailing deep learning-
based supervised and self-supervised methods have excelled in eliminating in-distribution …

Adversarial machine learning (AML) is a class of data manipulation techniques that cause
alterations in the behavior of artificial intelligence (AI) systems while going unnoticed by …

Learning adversarially robust models require invariant predictions to a small neighborhood
of its natural inputs, often encountering insufficient model capacity. There is research …

The phenomenon of adversarial examples is one of the most attractive topics in machine
learning research these days. These are particular cases that are able to mislead neural …

Deep learning-based image denoising techniques often struggle with poor generalization
performance to out-of-distribution real-world noise. To tackle this challenge, we propose a …

Speckle removal aims to smooth noise while preserving image boundaries and texture
information. In recent years, speckle removal models based on deep learning methods have …

Cliffside carving images are often affected by various types of noise, such as uneven
lighting, shadows, dust, and weathering, which impair the clarity and detail of the images …