In agriculture, fruit picking robots on the ground have difficulty adapting to the terrain
conditions of mountain orchards and cannot pick longan fruit from tall longan trees. In this …

Blind image deblurring is still a challenging problem due to the inherent ill-posed properties.
To improve the deblurring performance, many supervised methods have been proposed …

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 …

Blind deblurring has attracted much interest with its wide applications in reality. The blind
deblurring problem is usually solved by estimating the intermediate kernel and 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 …

Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …

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 …

In this study, a general network model called multi-progressive image deblurring network is
proposed to correct blurring artifacts and local imaging details in underwater images. As a …

Non-blind deblurring methods achieve decent performance under the accurate blur kernel
assumption. Since the kernel uncertainty (ie kernel error) is inevitable in practice, semi-blind …

We observe a common characteristic between the classical propagation-based image
matting and the Gaussian process (GP)-based regression. The former produces closer …