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We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural...

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of t...

Multiwavelets are new addition to the body of wavelet theory. There are many types of symmetric multiwavelets such as Geronimo-Hardin-Massopust (GHM) and Chui-Lian (CL) multiwavelets. However, the matrix filter generatin...

In this paper two images compression techniques with loss based on the method used by the compression Standard Joint Photographic Experts Groups (JPEG) and on the Quadtree adaptive partitioning method are presented and c...

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinatio...

Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the differenc...

The problem of efficient signal communication at low data rates involves, in general, the encoding of the source for maximum data compression at the transmitter end, and the reconstruction using the received information ...

Suppose x is an unknown vector in Ropf m (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform...