Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

In this paper we propose a generic recursive algorithm for improving image denoising
methods. Given the initial denoised image, we suggest repeating the following “SOS” …

Dictionary learning (DL) techniques aim to find sparse signal representations that capture
prominent characteristics in a given data. Such methods operate on a data matrix Y∈ RN× …

In plug-and-play (PnP) regularization, the knowledge of the forward model is combined with
a powerful denoiser to obtain state-of-the-art image reconstructions. This is typically done by …

Most motion-blurred images captured in the real world have spatially-varying point-spread
functions, and some are caused by different positions and depth values, which cannot be …

Image denoising is a longstanding research topic in low-level visions. The model-based
methods mainly depend on certain handcrafted prior terms to regularize the image …

Positron emission tomography (PET) reconstruction has become an ill-posed inverse
problem due to low-count projection data, and a robust algorithm is urgently required to …

Recent work in image processing suggests that operating on (overlapping) patches in an
image may lead to state-of-the-art results. This has been demonstrated for a variety of …

Positron emission tomography (PET) reconstruction becomes an ill-posed inverse problem
due to the low-count projection data (sinogram). In this paper, we leverage DeepRED from a …

Image filtering is to retain the details of the image as much as possible and meanwhile
suppress the noise pollution to great extent. This study presents an image filtering using the …