For medical image analysis, there is always an immense need for rich details in an image.
Typically, the diagnosis will be served best if the fine details in the image are retained and …

Light field (LF) photography is an emerging paradigm for capturing more immersive
representations of the real world. However, arising from the inherent tradeoff between the …

In this paper, we propose a new image super-resolution (SR) approach based on a
convolutional neural network (CNN), which jointly learns the feature extraction, upsampling …

Conventional supervised super-resolution (SR) approaches are trained with massive
external SR datasets but fail to exploit desirable properties of the given test image. On the …

This paper presents a fast, high-performance method for super resolution with external
learning. The first contribution leading to the excellent performance is a bimodal tree for …

There is a great leap in objective accuracy on image super-resolution, which recently brings
a new challenge on image super-resolution with larger up-scaling (eg 4×) using pixel based …

Given a low-resolution (LR) image, multi-modal image super-resolution (MISR) aims to find
the high-resolution (HR) version of this image with the guidance of an HR image from …

Optimization algorithms are implemented for making the field of machine learning more
efficient by comparing various solutions until an optimum or a satisfactory answer is found to …

A bilinear interpolation technique is proposed for flexible representations of quantum
images (FRQIs). In this process, several quantum modules were developed, including …

Convolutional neural networks (CNN) have enabled significant improvements in pedestrian
detection owing to the strong representation ability of the CNN features. However, it is …