Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-
resolution image from its low-resolution observation. To regularize the solution of the …

Cross-scene regression tasks, such as congestion level detection and crowd counting, are
useful but challenging. There are two main problems, which limit the performance of existing …

针对目前提高图像分辨率的卷积神经网络存在的特征提取尺度单一以及梯度消失等问题,
提出了多尺度残差网络的单幅图像超分辨率重建方法. 采用多尺度特征提取和特征信息融合 …

Compressive sensing (CS) is a recently emerging technique and an extensively studied
problem in signal and image processing, which suggests a new framework for the …

摘要提出了一种基于多尺度递归网络的图像超分辨率网络模型, 该模型主要由多个多尺度特征
映射单元级联而成, 每个单元分别包含一组不同尺度的特征提取层, 一个融合层以及一个特征 …

Face sketch synthesis is useful and profitable in digital entertainment. Most existing face
sketch synthesis methods rely on the assumption that facial photographs/sketches form a …

Recently, deep learning methods have made a significant improvement in compressive
sensing image reconstruction task. In the existing methods, the scene is measured block by …

The main target of Single image super-resolution is to recover high-quality or high-resolution
image from degraded version of low-quality or low-resolution image. Recently, deep …