Using diffusion models to solve inverse problems is a growing field of research. Current
methods assume the degradation to be known and provide impressive results in terms of …

Blind deconvolution problems are severely ill-posed because neither the underlying signal
nor the forward operator are not known exactly. Conventionally, these problems are solved …

Exploring motion information is important for the motion deblurring task. Recent the window-
based transformer approaches have achieved decent performance in image deblurring …

Quantitative acoustic microscopy (QAM) forms two-dimensional (2D) quantitative maps of
acoustic properties of thin tissue sections at a microscopic scale (m) using very-high …

Blind image deconvolution is a challenging issue in image processing. In blind image
deconvolution, the typical approach involves iteratively estimating both the blur kernel and …

The problem of blind image deblurring remains a challenging inverse problem, due to the ill-
posed nature of estimating unknown blur kernels and latent images within the Maximum A …

Existing learning-based image deblurring algorithms tend to focus on single source of image
information, and the network structure and dynamic scene blur characteristics make it …

Blind image deblurring is a challenging issue in image processing. In blind image
deblurring, the typical approach involves iteratively estimating both the blur kernel and latent …

The past two decades have seen photography shift from the hands of professionals to that of
the average smartphone user. However, fitting a camera module in the palm of your hand …

These days, one of the main challenges with photos is distortion. This has an impact on
numerous fields, including microscopy, medical imaging, astronomy, remote sensing, and …