|Table of Contents|

Remote Sensing Image Super-resolution Reconstructionin Multi-scale Compressed Sensing Framework(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年01期
Page:
39-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Remote Sensing Image Super-resolution Reconstructionin Multi-scale Compressed Sensing Framework
Author(s):
Chen Weiye; Sun Quansen
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
remote sensing image; super-resolution reconstruction; multi-scale; compressed sensing
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2017.01.007
Abstract:
The traditional compressed sensing based super-resolution reconstruction algorithm regards images as a single scale without considering that different scale image patches may have different discriminant information. To effectively utilize the scale characteristics of remote sensing images,a new remote sensing image super-resolution reconstruction algorithm in the multi-scale compressed sensing framework was proposed. First,image patches were clustered to construct multi-scale training sample sets. Next,the Fisher criterion was used to learn a discriminative dictionary containing the classification information of remote sensing images. Then,the acquisition process of the low-resolution image was estimated by the construction method of the measurement matrix in compressed sensing. Finally,the sub-region images in the multi-scale mode were reconstructed by using the discriminant dictionary. The experimental results demonstrate that it is effective to introduce multi-scale compressed sensing into image super-resolution reconstruction. The proposed algorithm outperforms other existing algorithms both in visual qualities and evaluation criteria.

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