A general framework for solving image inverse problems with piecewise linear estimations is
introduced in this paper. The approach is based on Gaussian mixture models, which are …

We introduce a class of inverse problem estimators computed by mixing adaptively a family
of linear estimators corresponding to different priors. Sparse mixing weights are calculated …

Image super-resolution (SR), a process to enhance image resolution, has important
applications in satellite imaging, high-definition television, medical imaging, and so on …

Recently, the residual learning strategy has been integrated into the convolutional neural
network (CNN) for single image super-resolution (SISR), where the CNN is trained to …

In this paper, we consider the problem of single image super-resolution and propose a novel
algorithm that outperforms state-of-the-art methods without the need of learning patches …

Image super-resolution involves the estimation of a high-resolution image from one or
multiple low resolution images. It is widely used in medical imaging, satellite imaging, target …

Recently sparse representations over learned dictionaries have been proven to be a very
successful representation method for many image processing applications. This paper …

ABSTRACT In our recent work [6], we proposed an algorithm for image upsampling based
on alternation of two procedures: spatially adaptive filtering in image domain and projection …

This paper proposes a novel method that combines the discrete wavelet transform (DWT)
and example-based technique to reconstruct a high-resolution from a low-resolution image …

This paper proposes a selective data pruning-based compression scheme to improve the
rate-distortion relation of compressed images and video sequences. The original frames are …