Abstract
The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB.
Subject
General Earth and Planetary Sciences
Cited by
23 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献