Abstract
Image super-resolution has wide applications in biomedical imaging, computer vision, image recognition, etc. In this paper, we present a fast single-image super-resolution method based on deconvolution strategy. The deconvolution process is implemented via a fast total variation deconvolution (FTVd) method that runs very fast. In particular, due to the inaccuracy of kernel, we utilize an iterative strategy to correct the kernel. The experimental results show that the proposed method can improve image resolution effectively and pick up more image structures. In addition, the speed of the proposed method is fast.
Highlights
The process of estimating a high-resolution (HR) image from one or multiple low-resolution (LR) images is often referred to as image super-resolution
According to the number of low-resolution images, image super-resolution can be divided into two categories: one is single-image super-resolution, and the other is multiple-image superresolution
Due to multiple-image super-resolution needs more than one input image, it cannot deal with the situation when only one image is inputted
Summary
The process of estimating a high-resolution (HR) image from one or multiple low-resolution (LR) images is often referred to as image super-resolution. When given a low-resolution image, learning-based methods can get a high-resolution image through using the learnt mapping relationship between the two dictionaries. These methods obtain good visual results, they rely on the two training dictionaries and cannot change the magnification factor arbitrarily. We propose a new single image superresolution method based on deconvolution strategy. We develop an iterative strategy to adjust the blur kernel and estimate the final high-resolution image via a reconstruction method.
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