Single image super-resolution by approximated Heaviside functions
This is an incremental improvement for applications like medical and satellite imaging.
The paper tackled single image super-resolution by modeling images as smooth and non-smooth components using approximated Heaviside functions, with an L1 model for sparsity, and reported effectiveness in comparisons with existing methods.
Image super-resolution is a process to enhance image resolution. It is widely used in medical imaging, satellite imaging, target recognition, etc. In this paper, we conduct continuous modeling and assume that the unknown image intensity function is defined on a continuous domain and belongs to a space with a redundant basis. We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively. Due to sparsity of the non-smooth components, a $L_{1}$ model is employed. In addition, we apply the proposed iterative model to image patches to reduce computation and storage. Comparisons with some existing competitive methods show the effectiveness of the proposed method.