This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Human face three-dimensional (3D) reconstruction is a challenging problem. In this paper, we propose a human face fast- 3D- reconstruction method based on image processing with a single image. Shape from shading (SFS) is chosen to reconstruct the human face. First, SFS theory is introduced. It has the advantage of fast 3D reconstruction and only need a single image. Secondly, because the noise will affect the 3D reconstruction result greatly, wavelet transform and wavelet packet transform are introduced and used in image denoising respectively. The experiment has shown that the method based on wavelet transform produces the best denoising result than wavelet packet transform. At last, a human face 3D reconstruction algorithm based on a single image is proposed. The experimental results show that a human face 3D model can be reconstructed in fast by proposed algorithm.

Human Face Fast-3D-Reconstruction from a Single Image

Image-based Rendering technology with plenoptic function as core theoretical foundation,This paper discusses the basic principle of Image Illumination Coherence.Combined with Witted lighting model,authors briefly introduced a new global illumination model and presented an new aproach of Computing reflection brightness and transmission brightness.

Image Illumination Coherence Based on Augment reality

To improve the precision of imaging three-dimensional reconstruction based on shape from shading,an improved illumination model was proposed.This model not only improved the diffusion reflectance component,but also considered the influence of specular reflectance component with linear superposition.The variables of the model were dispersed by using finite difference method and the model was solved by using Jacobi iteration.Then,the three-dimensional reconstruction height was obtained.By taking the metallic ball image as example and reconstructing its three-dimensional image,the experimental results showed that the relative height error after using improved illumination model was dropped by 12.19 % compared to the traditional methods.

Improved Illumination Model of Shape from Shading and Its Realization

For the purpose of further processing like segment,it is necessary to remove noisy while keeping the edge's information in CT image processing.The conventional anisotropic diffusion is effective in removing noises,but it often destructs the structural information of the object in the image because of a sequence of inaccurate diffusions.A new anisotropic diffusion approach based on edge enhancement operator is proposed.At first,gradient threshold K is calculated by using GHAD(Gradient Histogram-Based Anisotropic Diffusion) for anisotropic diffusion.Secondly,an edge enhancement operator is incorporated into the diffusion steps,to obtain the edge details.Experiments on CT images indicate effective preservation of edge,removal of noise by our scheme.

Anisotropic Diffusion Filtering Based on Edge Enhancement Operator

For the reason that the conventional illumination models are empirical and non-linear, the traditional shape from shading (SFS) methods with conventional illumination models are always divergent in the process of iteration and are difficult to initialize the parameters of the illumination model. To overcome these disadvantages, a new approach based on the neural network for indirect SFS is proposed in this paper. The new proposed approach applies a series of standard sphere pictures, in which the gradients of the sphere can be calculated, to train a neural network model. Then, the gradients of the reconstructed object pictures, which are taken in the similar circumstances as that of the standard sphere pictures, can be obtained from the network model. Finally, the height of the surface points can be calculated. The results show that the new proposed method is effective, accurate and convergent.

A Neural Network Approach for Indirect Shape from Shading

Abstracf For the advantages of non-measurement, quickness, convenience and, most especially, the capability of reconstructing an object surface from just one image, the method of SFS (Shape from Shading) has play an important role in RE (Reverse engineering), object location, pattern recognition and related fields. Recently, many scholars focus; on the problem of how to improve the accuracy of SFS by improving the algorithm of reconstruction. However, rare satisfied results of SFS by improving arithmetic were obtained. In this paper, a new idea is offered to improve the accuracy of SFS by referring to a more general illumination model, the Oren-Nayrtr model, which is available for image rendering field. Meanwhlile, the Oren-Nayar model is improved to satisfy the requirement of iterative approach and the component of specular reflection is added to satisfy the requirement of less rough or even smooth surface. Through taking the place of the traditional illumination model, the Lambert model, by an improved Oren-Nayar model, the accuracy of SFS is improved obviously. In addition, the robust of SFS is improved by transforming the nonlinear factor to linear factor in the illumination model.

A more general illumination model for surface reconstruction

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A Neural Network Approach for Indirect Shape from Shading

A more general illumination model for surface reconstruction