Approximate Image Reconstruction in Landscape Reflection Imaging

Abstract

Simple reflection imaging of landscape (scenery or extended objects) poses the inverse problem of reconstructing the landscape reflectivity function from its integrals on some particular family of spheres. Such data acquisition is encoded in the framework of a Radon transform on this family of spheres. In spite of the existence of an exact inversion formula, the numerical landscape reflectivity function reconstitution is best obtained with an approximate but judiciously chosen reconstruction kernel. We describe the working of this reflection imaging modality and its theoretical handling, introduce an efficient and stable image reconstruction algorithm, and present simulation results to prove the validity of this choice as well as to demonstrate the feasibility of this imaging process.

Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm

“…The vision research based on psychology is to describe and analyze the relationship between brightness feeling, color feeling, and physical stimulation as much as possible, express how people “feel light” and “understand color” with mathematical model, and then apply the visual expression to HDR image to expect to get the same perception result on LDR display device. In order to further study this problem, we draw the mapping curves of the two visual perception models in the same coordinate system [ 21 ]. To make the algorithm more popular, we used low dynamic range light when displaying on low dynamic range display devices, and we assumed that the output range of accepted values is between 0 and 1.…”