Abstract
Vivid main structure and rich texture detail are important factors with which to determine the quality of high-resolution images after super-resolution (SR) reconstruction. Owing to the loss of high-frequency information in the process of SR reconstruction and the limitation of the accurate estimation of the unknown information in the inversion process, a gap still exists between the high-resolution image and the real image. The main structure can better preserve the edge structure of the image, and detail boosting can compensate for the missing high-frequency information in the reconstruction process. Therefore, a novel single remote-sensing image SR reconstruction method based on multilevel main structure and detail boosting (MMSDB-SR) is put forward in this paper. First, the multilevel main structure was obtained based on the decomposition of the remote-sensing image through use of the relative total variation model. Subsequently, multilevel texture detail information was obtained by a difference process. Second, the multilevel main structure and texture detail were reconstructed separately. The detail-boosting function was used to compensate for the missing high-frequency details in the reconstruction process. Finally, the high-resolution remote-sensing image with clear edge and rich texture detail can be obtained by fusing the multilevel main structure and texture-detail information. The experimental results show that the reconstructed high-resolution image has high clarity, high fidelity, and multi-detail visual effects, and the objective evaluation index exhibits significant improvement. Actual results show an average gain in entropy of up to 0.34 dB for an up-scaling of 2. Real results show an average gain in enhancement measure evaluation of up to 2.42 for an up-scaling of 2. The robustness and universality of the proposed SR method are verified.
Highlights
Super-resolution (SR) reconstruction is the technology of obtaining high-resolution (HR) images or sequences from one or more low-resolution (LR) observation images by means of signal processing [1].SR reconstruction is widely used in remote sensing, video surveillance, medical diagnosis, militaryRemote Sens. 2018, 10, 2065; doi:10.3390/rs10122065 www.mdpi.com/journal/remotesensing Remote FOR PEER REVIEW Remote Sens.Sens. 2018, 22of of 24[1]
The existing single-image SR reconstruction methods can be divided into four categories [6,7]: SR models based on interpolation, based on reconstruction, based on deep learning, and based on information enhancement
To fully extract the information carried by a single image, multilevel texture detail is acquired through multilevel main structure difference processing
Summary
Super-resolution (SR) reconstruction is the technology of obtaining high-resolution (HR) images or sequences from one or more low-resolution (LR) observation images by means of signal processing [1]. SR reconstruction is widely used in remote sensing, video surveillance, medical diagnosis, military. SR reconstruction is widely used in remote sensing, video surveillance, medical diagnosis, reconnaissance, and other fieldsfields [2]. In the of remote sensing, with thethe rapid development of topics theof field of image reconstruction. The resolution of optical satellite images can be improved of reconstruction technology at a small cost to meet more refinedmore applications. SR reconstruction technology at a economic small economic cost the to increasingly meet the increasingly refined of remote-sensing images [3,4,5]. Owing to to the the fact that the complementary information contained in multiple generate images can make up for the high-frequency information information lost in the reconstruction process, it has images researchers andand promising results have have been forthcoming.
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