As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Images taken under the low-light condition often contain blur and saturated pixels at the
same time. Deblurring images with saturated pixels is quite challenging. Because of the …

Blind deblurring has received considerable attention in recent years. However, state-of-the-
art methods often fail to process saturated blurry images. The main reason is that saturated …

Deblurring night blurry images is difficult, because the common-used blur model based on
the linear convolution operation does not hold in this situation due to the influence of …

Blind image deconvolution (BID) is about recovering a latent image with sharp details from
its blurred observation generated by the convolution with an unknown smoothing kernel …

Cervical cytopathology image refocusing is important for addressing the problem of defocus
blur in whole slide images. However, most of current deblurring methods are developed for …

Image deblurring and its counterpart blind problem are undoubtedly two fundamental tasks
in computational imaging and computer vision. Interestingly, deterministic edge-preserving …

Star sensors usually have a fixed exposure time to guarantee detection of adequate
navigation stars. In a high dynamic environment, star images suffer from degradation due to …

Conventional deconvolution methods utilize hand-crafted image priors to constrain the
optimization. While deep-learning-based methods have simplified the optimization by end-to …

The most active study topic in the field of computer vision is dealing with saturation in image
deblurring. The Deep Multi Patch Framework (DMPHN) is primarily utilized in dynamic …