Edge-Preserving Single Remote-Sensing Image Super-resolution Using Sparse Representations

被引：0

作者：

Barman T. ^{[1
]}

Deka B. ^{[1
]}

Mullah H.U. ^{[1
]}

机构：

[1] Department of Electronics and Communication Engineering, Tezpur University, Assam, Tezpur

来源：

SN Computer Science | / 4卷 / 3期

关键词：

CUDA-enabled GP-GPU; Dictionary learning; SIFT; Sparse representations; Super-resolution Imaging;

D O I：

10.1007/s42979-023-01764-7

中图分类号：

学科分类号：

摘要：

Multispectral (MS) sensors are mostly of low resolution (LR) and fail to give promising results in remote-sensing applications. In the recovery of edge information from LR images, the sparse representation-based single image super-resolution (SISR) employing patch-based dictionary alone does not give satisfactory results. To overcome this, we propose a parallel SISR framework based on edge-preserving dictionary learning and sparse representations on compute unified device architecture (CUDA)-enabled graphics processing units (GPU). To recover edges, multiple coupled dictionaries, namely, the scale-invariant feature transform (SIFT) keypoints and non-keypoints patch-based dictionaries, are learned. A joint reconstruction model is also designed based on SIFT keypoints-guided patch sparsity and non-local total variation (NLTV)-based gradient sparsity. Simulation results show that the proposed method not only performs better than state-of-the-art methods in terms of visual quality and objective criteria, but also enhances the speed, implying a great potential for real-time applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

引用

共 46 条

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