Sep 30, 2021 · In this paper, we propose a lightweight network that uses conv-LSTM for feature fusion (LFN) to improve image super-resolution performance and save the number ...
In this paper, we propose a lightweight network that uses conv-LSTM for feature fusion (LFN) to improve image super-resolution performance and save the ...
Using Conv-LSTM to Refine Features for Lightweight Image Super-Resolution Network. 点击次数：1. 发表刊物：. Lecture Notes in Computer Science (including ...
Sep 26, 2024 · In order to reduce FLOPs, a 1×1 convolution is used to reduce the number of channels, and then strided convolution and max pooling are used to ...
Deep convolutional neural networks have been successfully applied to image super resolution. In this paper, we propose a multi-context fusion learning based ...
This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing.
Sep 9, 2023 · Its main task is to transform a low-resolution (LR) image into a high-resolution (HR) variant, simultaneously enriching the image's fine details ...
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Jun 15, 2024 · This paper proposes an efficient model (BCRN) based on BSConv and the ConvNeXt residual structure for single image super-resolution, which achieves superior ...
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A novel Hybrid Pixel-Unshuffled Network (HPUN) is proposed by introducing an efficient and effective downsampling module into the SR task.
Sep 15, 2020 · Abstract. This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.