Reconstruction of Fine Grayscale Image with Low Sampling Rate Based on the Salience Analysis

Abstract

This paper introduces a new method for image reconstruction, which mainly aims at fine grayscale image reconstruction and it is based on visual salience analysis. Considering there is much difference of the texture and details in one image, so the feature of each pixel should be employed. In this paper, an algorithm is proposed to seize this kind of feature and use it to help image reconstruction. For two-dimensional fine grayscale image reconstruction, LC+ASPL algorithm can get better results. LC means Luminance contrast, which is used as a criterion to describe salience of single pixel. ASPL is a weighted image reconstruction algorithm, it is obtained from the method of Smoothed Projected Landweber. For data sampling, analyze the LC feature of the image in advance, then according to the analysis results and Block Compressed Sensing (BCS)theory, choose the sampling data selectively. Give higher weight to high salient area and lower weight to matching areas. For the process of image reconstruction, lock the salient regions of the image for further analysis and processing. It has been proved that this method of image measurement and reconstruction can help to reconstruct the 2D fine grayscale image more accurately and reproduce the details more clearly with fewer data. This method can be applied to medical image reconstruction, like CT and MRI reconstruction. For one thing, reduce the sampling data can reduce the scanning time for the patient; for another thing, finely reconstruction can help the doctors diagnose disease. This method can also give some theoretical guidance for fine image reconstruction.