Abstract
Single-image super-resolution (SISR) is a resolution enhancement technique and is known as an ill-posed problem. Motivated by the idea of pan-sharping, we propose a novel variational model for SISR. The structure tensor of the input low-resolution image is exploited to obtain the gradient of an imaginary panchromatic image. Then, by constraining the gradient consistency, the image edges and details can be better recovered during the procedure of restoration of high-resolution images. Besides, we resort to the nonlocal sparse and low-rank regularization of image patches to further improve the super-resolution performance. The proposed variational model is efficiently solved by ADMM-based algorithm. We do extensive experiments in natural images and remote sensing images with different magnifying factors and compare our method with three classical super-resolution methods. The subjective visual impression and quantitative evaluation indexes both show that our method can obtain higher-quality results.
Highlights
Image super-resolution (SR) is one of the most fundamental problems in the field of image processing, which aims to reconstruct clear and accurate high-resolution images from degraded low-resolution images
We first evaluate the sensitivity of the proposed algorithm with respect to the main parameters, that is, μ1, μ2, μ3, and μ4. en, to demonstrate the SR effectiveness on both natural images and remote sensing images with different magnifying factors (2 and 4), we compare our method with the Bicubic interpolation method, sparsecoding-based method (SC) [22], and its back-projection enhanced version (SCBP) [35]
We evaluate the outcome of various methods by using quantitative indexes: peak signalto-noise ratio (PSNR), root mean square error (RMSE), and structure similarity index (SSIM) [36]; they are calculated on
Summary
Image super-resolution (SR) is one of the most fundamental problems in the field of image processing, which aims to reconstruct clear and accurate high-resolution images from degraded low-resolution images. SISR is suitable for more scenarios, which enhances the resolution of one input image based on some prior information [9, 10]. Reconstruction-based SISR methods [14,15,16] generally utilize image priors to restrict the possible solution space with an advantage of recovering sharp details. These methods are usually timeconsuming, and their performance suffers a rapid degradation when the amplification factor increases. By utilizing the structure tensor [31] of input image, we can construct the gradient of the PAN image; we build a variational model combined with low-rank and sparse representation of similar patches to effectively fuse the constructed PAN information with LR image and obtain HR image.
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