Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying
nature of defocus blur, characterized by per-pixel point spread functions (PSFs). Existing …

Multi-scale and multi-patch deep models have been shown effective in removing blurs of
dynamic scenes. However, these methods still have one major obstacle: manually designing …

Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a
defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus …

The existing non-blind deblurring methods are mostly susceptible to noise in the given
blurring kernel, which is usually estimated from the observed image. This will produce …

Defocus blur is one kind of blur effects often seen in images, which is challenging to remove
due to its spatially variant amount. This paper presents an end-to-end deep learning …

In recent years, there has been a growing interest in combining learnable modules with
numerical optimization to solve low-level vision tasks. However, most existing approaches …

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 …

Mask-based lensless imaging is an emerging imaging modality, which replaces the lenses
with optical elements and makes use of computation to reconstruct images from the …

Nonblind image deconvolution (NID) is about restoring the latent image with sharp details
from a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep …