Most existing non-blind image deconvolution methods assume that the given blurring kernel
is error-free. In practice, blurring kernel often is estimated via some blind deblurring …

Image blur kernel estimation is critical to blind image deblurring. Most existing approaches
exploit handcrafted blur features that are optimized for a certain uniform blur across the …

Blind motion deblurring methods are primarily responsible for recovering an accurate
estimate of the blur kernel. Non-blind deblurring (NBD) methods, on the other hand, attempt …

In this paper, we present a deep convolutional neural network to capture the inherent
properties of image degradation, which can handle different kernels and saturated pixels in …

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 …

Most non-blind deconvolution methods are developed under the error-free kernel
assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great …

The maximum a posterior (MAP)-based blind deconvolution framework generally involves
two stages: blur kernel estimation and non-blind restoration. For blur kernel estimation …

As an integral component of blind image deblurring, non-blind deconvolution removes
image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature …

Blind image deblurring is a challenging image processing problem, and a proper solution for
this problem has many applications in the real world. This is an ill-posed problem, as both …

We present a simple and effective approach for non-blind image deblurring, combining
classical techniques and deep learning. In contrast to existing methods that deblur the …