May 15, 2017 · In this paper, we propose a multi-scale super resolution (MSSR) network. Our network consists of multi-scale paths to make the HR inference.
May 10, 2018 · Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution.
A multi-scale super resolution (MSSR) network that consists of multi- scale paths to make the HR inference, which can learn to synthesize features from ...
SRCNN is one of the successful method for image super-resolution with a convolutional neural network. The network builds an end-to-end mapping between a pair of ...
Single image super-resolution via multi-scale fusion convolutional ...
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This paper proposes multiscale fusion convolutional neural network for single image superresolution. The network has the following two advantages: 1) the multi- ...
This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder (deconvolution) network for single image super-resolution (SISR) guided ...
Jun 10, 2024 · Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit ...
The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides themulti-context for image super-resolution, ...
This paper presents an improved CNN architecture for estimation of the original HR image through an end-to-end learning method in SISR algorithm.
Feb 19, 2022 · In this paper, we propose an end-to-end single-image super-resolution neural network by leveraging hybrid multi-scale features of images.