Abstract
There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.
Highlights
We address the problem of generating a high-resolution (HR) image given multiple low-resolution (LR) images [1,2]
In the super-resolution reconstruction technology applied in optical satellite remote-sensing image processing, homologous or heterogeneous sequences of remote-sensing images with the same area are used for super-resolution (SR) reconstruction to improve image spatial
The core idea of super-resolution reconstruction is to increase the spatial resolution by using the time bandwidth, which is to achieve the transformation from temporal resolution to spatial resolution
Summary
We address the problem of generating a high-resolution (HR) image given multiple low-resolution (LR) images [1,2] At present, this is a research hotspot in the remote-sensing image processing field. SR reconstruction technology can enhance the spatial resolution of satellite imagery at a lower economic cost by making full use of satellite remote-sensing image data without increasing hardware investment. Spatio-temporal remote-sensing image can provide non-redundant information, enhance complementary information in spatial domain, and improve texture-feature representation. It is an effective method for the super-resolution reconstruction to take advantage of the important information provided by the spatio-temporal remote-sensing image. The research results have important theoretical significance and practical value [6]
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