This paper considers the conditional fault tolerance, h-super connectivity κ and h-super edge-connectivity λ of the hierarchical cubic network HCNn, an attractive alternative network to the hypercube, and shows κ(HCNn) = λ (HCNn) = 2 (n+ 1− h) for any h with 0 ≤ h ≤ n− 1. The results imply that at least 2(n+ 1− h) vertices or edges have to be removed from HCNn to make it disconnected with no ve...

Preface Graphs are often used as a model for networks, for example transport networks, telecommunication systems, a network of servers and so on. Typically, the networks and thus the representing graphs are connected; that means, that there exists a path between all pairs of two vertices in the graph. But sometimes an element of the network fails, for the graphs that means the removal of a vert...

Super-resolution of a single image is a severely ill-posed problem in computer vision. It is possible to consider solving this problem by considering a total variation based regularization framework. The choice of total variation based regularization helps in formulating an edge preserving scheme for super-resolution. However, this scheme tends to result in a piece-wise constant resultant image...

Hyperspectral (HS) super-resolution reconstruction is an ill-posed inversion problem, for which the solution from reconstruction constraint is not unique. To address this, an HS image super-resolution method is proposed to first utilize the joint regulation of spatial and spectral nonlocal similarities. We then fused the HS and panchromatic images with sparse regulation. With these two regulati...

In this paper we propose two algorithms of super resolution techniques. We introduce the Iterative Back Projection (IBP) algorithm in the case of deblurring images and second algorithm consist an edge-enhancing superresolution algorithm using anisotropic diffusion technique. Because we solve the super-resolution problem by incorporating anisotropic diffusion and IBP, these techniques does more ...

In this paper we propose a maximum a posteriori (MAP) framework for the super resolution problem, i.e. reconstructing high-resolution images from shifted, low-resolution degraded observations. In this framework the restoration, interpolation and registration subtasks of this problem are preformed simultaneously. The main novelties of this work are the use of a new hierarchical non-stationary ed...

We present a novel hybrid scheme called “hyper-resolution” that integrates image probability-filtering-based interpolation and improved Poisson maximum a posteriori (MAP) super-resolution to respectively enhance high spatial and spatial-frequency resolutions of a single down-sampled image. A new approach to interpolation is proposed for simultaneous image interpolation and smoothing by exploiti...