In this paper, we propose a new learning-based approach for super-resolving an image
captured at low spatial resolution. Given the low spatial resolution test image and a …

We propose a single‐frame, learning‐based super‐resolution restoration technique by
using the wavelet domain to define a constraint on the solution. Wavelet coefficients at finer …

We propose a novel, learning-based algorithm for image super-resolution. First, an optimal
distance-based weighted interpolation of the image sequence is performed using a new …

We present a learning-based single image super-resolution (SISR) method to obtain a high
resolution (HR) image from a single given low resolution (LR) image. Our method gives …

This paper proposes a framework for single-image super-resolution. The underlying idea is
to learn a map from input low-resolution images to target high-resolution images based on …

Multi-frame image super-resolution is a procedure which takes several noisy low-resolution
images of the same scene, acquired under different conditions, and processes them …

This paper addresses the problem of single image super-resolution (SR), which consists of
recovering a high-resolution image from its blurred, decimated, and noisy version. The …

It has been widely acknowledged that learning-and reconstruction-based super-resolution
(SR) methods are effective to generate a high-resolution (HR) image from a single low …

Learning based image super-resolution (SR) has been a striking area of research for
generating high-resolution (HR) images from low-resolution (LR) images. A new in-scale …

In this paper, we propose a learning-based super resolution approach consisting of two
steps. The first step uses the kernel partial least squares (KPLS) method to implement the …