Image reconstruction based on fused features and perceptual loss encoder-decoder residual network for space optical remote sensing images compressive sensing

doi:10.1109/ACCESS.2021.3069086

CIOMP OpenIR

	Image reconstruction based on fused features and perceptual loss encoder-decoder residual network for space optical remote sensing images compressive sensing
	S. Xiao; S. Wang and L. Chang
	2021
发表期刊	IEEE Access
ISSN	21693536
卷号	9 页码:50413-50425
摘要	Compressive sensing (CS) technology is introduced into space optical remote sensing image acquisition stage, which could make wireless image sensor network node quickly and accurately obtain images in the case of two constraints of limited battery power and expensive sensor costs. On this basis, in order to further improve the quality of CS image reconstruction, we propose fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet) for image reconstruction. FFPL-EDRNet consists of a convolution layer and a reconstruction network. We train FFPL-EDRNet end-to-end, thus greatly simplifying the pre-processing and post-processing process and eliminating the block effect of reconstructed images. The reconstruction network is based on residual network, which introduces multi-scale feature extraction, multi-scale feature combination and multi-level feature combination. Feature fusion integrates low-level information with high-level information to reduce reconstruction error. The perceptual loss function based on pretrained InceptionV3 uses the weighted mean square error to define the loss value between the reconstructed image feature and the label image feature, which makes the reconstructed image more semantically similar to label image. In the measurement procedure, we use convolution to achieve block compression measurement, so as to obtain full image measurements. For image reconstruction, we firstly use a deconvolution layer to initially reconstruct the image and then use the residual network to refine the initial reconstructed image. The experimental results show that: In the case of measurement rates (MRs) of 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio (PSNR) = 27.502, 26.804, 24.593, 21.359 and structural similarity (SSIM) = 0.842, 0.816, 0.720, 0.568 of the reconstructed images obtained by FFPL-EDRNet. Therefore, Our FFPL-EDRNet could enhance the quality of image reconstruction. 2013 IEEE.
DOI	10.1109/ACCESS.2021.3069086
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收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65312
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	S. Xiao,S. Wang and L. Chang. Image reconstruction based on fused features and perceptual loss encoder-decoder residual network for space optical remote sensing images compressive sensing[J]. IEEE Access,2021,9:50413-50425.
APA	S. Xiao,&S. Wang and L. Chang.(2021).Image reconstruction based on fused features and perceptual loss encoder-decoder residual network for space optical remote sensing images compressive sensing.IEEE Access,9,50413-50425.
MLA	S. Xiao,et al."Image reconstruction based on fused features and perceptual loss encoder-decoder residual network for space optical remote sensing images compressive sensing".IEEE Access 9(2021):50413-50425.

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