We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore, high quality blind deblurring has remaine...

To improve the visual quality of blur images, deblurring techniques are desired, which also play an important role in character recognition and image understanding. The problem of recovering the clear scene text by exploiting the text field characteristics. A series of text-specific multiscale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the...

Gaussian particles provide a flexible framework for modelling and simulating three-dimensional star-shaped random sets. In our framework, the radial function of the particle arises from a kernel smoothing, and is associated with an isotropic random field on the sphere. If the kernel is a von Mises–Fisher density, or uniform on a spherical cap, the correlation function of the associated random f...

The course can be split in two parts. In the implementation part, the kernel estimation process as described in [1] has been studied. The algorithm has been tested against synthetic and real data; and its performance has been discussed. In the reading part, a number of papers have been covered from the list of papers discussed in the graduate level course CPSC 505 “Image Understanding – I: Imag...

Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using information inter-frame intra-frame images. Inaccurate blur kernel estimation will enlarge distortion estimated high-resolution image. Therefore, multi-frame blind super with unknown is more challenging. For purpose reducing impact inaccurate motion on super-resolved ...

In the proposed KJIT, there are two kernels: the inner kernel k for computing mean embeddings, and the outer Gaussian kernel κ defined on the mean embeddings. Both of the kernels depend on a number of parameters. In this section, we describe a heuristic to choose the kernel parameters. We emphasize that this heuristic is merely for computational convenience. A full parameter selection procedure...