Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable
Diffusion, less research attention is paid to its adversarial robustness. In this work, we study …

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 …

Uncertainty quantification is crucial for the deployment of image restoration models in safety-
critical domains, like autonomous driving and biological imaging. To date, methods for …

Deep learning, in general, focuses on training a neural network from large labeled datasets.
Yet, in many cases, there is value in training a network just from the input at hand. This is …

Training deep neural networks has become a common approach for addressing image
restoration problems. An alternative for training a" task-specific" network for each …

Denoising diffusion models have emerged as the go-to framework for solving inverse
problems in imaging. A critical concern regarding these models is their performance on out …

In recent years, the neural radiance field (NeRF) model has gained popularity due to its
ability to recover complex 3D scenes. Following its success, many approaches proposed …

Generative diffusion prior captured from the off-the-shelf denoising diffusion generative
model has recently attained significant interest. However, several attempts have been made …

Image restoration and enhancement is a process of improving the image quality by
removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) …