Recently, there has been extensive research interest in training deep networks to denoise
images without clean reference. However, the representative approaches such as …

Deep networks provide state-of-the-art performance in multiple imaging inverse problems
ranging from medical imaging to computational photography. However, most existing …

Blind and universal image denoising consists of using a unique model that denoises images
with any level of noise. It is especially practical as noise levels do not need to be known …

Recently developed deep-learning-based denoisers often outperform state-of-the-art
conventional denoisers, such as the BM3D. They are typically trained to minimizethe mean …

Most of existing image denoising methods learn image priors from either an external data or
the noisy image itself to remove noise. However, priors learned from an external data may …

Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems.
Among them, denoising is known to be particularly challenging for the DIP due to noise …

Since the introduction of adaptive steganography, most of the recent research works seek at
designing cost functions that are evaluated against steganalysis methods. While those …

We introduce a method for the fast estimation of data-adapted, spatially and temporally
dependent regularization parameter-maps for variational image reconstruction, focusing on …

We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson or
mixed Poisson-Gaussian noise. Many applications involve such a problem, ranging from …

Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training
of deep neural network Gaussian denoisers that outperformed classical non-deep learning …