Most of the previous sparse coding (SC) based super resolution (SR) methods partition the image into overlapped patches, and process each patch separately.
Our CSC-SR involves three groups of pa- rameters to be learned: (i) a set of filters to decompose the low resolution (LR) image into LR sparse feature maps; (ii).
Sep 3, 2019 · Then, the high-frequent part and low-frequent part were reconstructed by means of convolutional sparse coding method and cubic interpolation ...
Aug 3, 2019 · In this paper, we exploit the natural connection between CSC and Convolutional Neural Networks (CNN) to address CSC based image SR. Specifically ...
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Oct 22, 2024 · The performance of convolutional codes with pseudorandom interleaving in a general nonstationary Gaussian noise environment is analyzed.
We present a computationally efficient architecture for im- age super-resolution that achieves state-of-the-art results on images with large spatial extend.
Abstract: According to image super-resolution reconstruction for convolutional sparse coding model, a novel super-resolution reconstruction algorithm named ...
In this paper, we exploit the natural connection between CSC and Convolutional Neural Networks (CNN) to address CSC based image SR. Specifically, Convolutional ...
Missing: research | Show results with:research
Mar 4, 2020 · In this paper, we propose a modified CSC-SR method to single image super-resolution. The proposed method only needs to train one group of filers ...
This document describes research in the following three areas: (1) video test facilities development, (2) morphological coding of images, and (3) two band image ...