Efficient Resolution Enhancement of JPEG2000 Compressed Multispectral Images Using Deep Super-resolution Methods

Multispectral imaging is one of the most important Earth observation techniques in remote sensing. Although their advantages, multispectral imaging systems continuously capture images resulting in enormous data volumes. To this end, some of the multispectral satellites use JPEG2000 based compression methods. However, the images that are compressed at low bit-rates contain compression artifacts and may present low-performances in remote sensing applications such as target detection and classification. In this paper, we propose the usage of three single image super-resolution methods, SRResNet, EDSR, and WDSR, for the resolution enhancement of JPEG2000 compressed images. First, the multispectral image is subsampled at factor of 4 along both spatial axes, and then the resulting image is compressed with JPEG2000. Finally, super-resolution methods are performed to improve the resolution and reduce the compression artifacts. Experiments were carried out on the Onera dataset shared by IEEE GRSS. The results are compared in terms of quality metrics such as signal-to-noise ratios, mean spectral angle, maximum spectral angle, and maximum absolute difference. Experimental results demonstrate that the proposed approaches provide higher quality metrics and better visual performance compared to bicubic upsampling.