Abstract
To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the high-resolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method.
Highlights
Image resolution refers to the number of pixels contained in an image per unit area
The projection onto convex set (POCS) SR reconstruction algorithm is used to obtain high-quality remote sensing data, which can meet the requirements of agricultural data sources
The experimental images with some knife-edge areas can be selected by the ADS 40 remote sensing image with the size of 200 × 200 (Example 1) and the unmanned aerial vehicle (UAV) image with the size of 800 × 800 (Example 2)
Summary
Image resolution refers to the number of pixels contained in an image per unit area. This parameter is an important factor used to evaluate the quality of remote sensing images. The initial image estimation by using the wavelet bicubic interpolation for POCS reconstruction algorithm; the experimental results are evident. The model is built by the sharpness of edge regions and the smoothness of smooth regions in the total initial image estimation by using the wavelet bicubic interpolation for POCS reconstruction variance of the image, as well as the prior information of the image ambiguity function. SR algorithm based on PSF estimation of Subsequently, LR remote sensing the cross-iteration method is used to solve the model, and the PSF and HR image are obtained. Exploration the relationship between the PSF of the HRmethod, image andblind the PSF and theimages reconstruction results of theofthree different SR methods Materials and Methods reconstruction results of the three different SR methods (proposed method, blind SR method, and bicubic interpolation method), in the simulated experiment and the real experiment, are compared
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