Multi-Image Super-Resolution for Thermal Images

被引：1

作者：

Rivadeneira, Rafael E. ^{[1
]}

Sappa, Angel D. ^{[1
,2
]}

Vintimilla, Boris X. ^{[1
]}

机构：

[1] Escuela Super Politecn Litoral, Fac Ingn Elect & Comp, CIDIS, ESPOL, Campus Gustavo Galindo Km 30-5 Via Perimetral, Guayaquil, Ecuador

[2] Comp Vis Ctr, Edifici 0,Campus UAB, Barcelona 08193, Spain

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 4 | 2022年

关键词：

Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block; RESOLUTION; RECONSTRUCTION;

D O I：

10.5220/0010899500003124

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches.

引用

页码：635 / 642

页数：8

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