Image super-resolution based on multifractals in transfer domain

Abstract

The goal of image super-resolution technique is to reconstruct high-resolution image with fine texture details from its low-resolution version.On Fourier domain,such fine details are more related to the information in the highfrequency spectrum. Most of existing methods do not have specific modules to handle such high-frequency information adaptively. Thus, they cause edge blur or texture disorder. To tackle the problems, this work explores image super-resolution on multiple sub-bands of the corresponding image, which are generated by NonSubsampled Contourlet Transform (NSCT). Different sub-bands hold the information of different frequency which is then related to the detailedness of information of the given low-resolution image.In this work, such image information detailedness is formulated as image roughness. Moreover, fractals analysis is applied to each sub-band image. Since fractals can mathematically represent the image roughness, it then is able to represent the detailedness (i.e. various frequency of image information). Overall, a multi-fractals formulation is established based on multiple sub-bands image. On each sub-band, different fractals representation is created adaptively. In this way, the image super-resolution process is transformed into a multifractal optimization problem. The experiment result demonstrates the effectiveness of the proposed method in recovering high-frequency details.