A simplex SISR utilizing upper-band spectrum prognosis with an alternative regularized Andrew's sine influence function

In order to achieving a fine spatial image, which are algebraically manufactured from either single crude resolution image or many crude resolution images for executing by either computer vision algorithmic techniques or Digital Image Processing (DIP) algorithmic techniques, one of the most practical algorithmic techniques in the image enlargement operation is the Super Resolution Reconstruction (SRR), especially Single-Image Super-Resolution (SISR), which is established on the image enlargement algorithmic technique that can algebraically manufacture the fine spatial image from a single crude resolution image. In this paper, the image enlargement algorithmic technique established on SISR utilizing upper-band spectrum prognosis with an alternative regularized Andrew's Sine influence function due to the fact that this SISR algorithmic technique has great achievement and requires low computational time. Unfortunately, the classical regularized function C(x, y) in the upper-band prognosis process is algebraically build upon three adjusting parameters (b, h, k) thence the parameter adjustment is time consuming in order to bring its achievement maximum. Due to this obstacle, this article proposes an alternative regularized Andrew's Sine influence function, which is algebraically build upon only one parameter (T), contrary to three parameters like the classical regularized function C(x, y), for a simplex SISR utilizing upper-band spectrum prognosis. The simulated experimentation is analyzed on up to 14 classic images, which are tarnished by different noise category and the proposed simplex SISR algorithmic technique is proved to be dramatically low computation than the original SISR with identical achievement.