Study And Improvement On The Metropolis Light Transport Algorithm

Metropolis light transport algorithm is a kind of unbiased Markov Chain Monte Carlo Global illumination algorithm.This algorithm has the advantages of high efficiency and robustness in the scene of complicated illumination.Therefore,it is widely concerned in the generation of realistic graphics.But in the process of rendering,the random noise is caused by the mutation strategy or path selection problem.While sampling,after the Metropolis light transport algorithms find an efficient light path,the algorithm can fast generate other efficient paths by mutating in a small area and multiplexing the already known paths,to reduce the random noise from the complicated illumination scene.Metropolis sampling judge the acceptance or the rejection of the proposed samples by the result of acceptance probability,and step by step find the paths which have higher contribution values and more efficiently generate images.Therefore,the selection of the mutation strategy and the calculation of the acceptance probability can work well on the quality of the image generated by Metropolis light transport.Based on the influential factors mentioned above,three improved Metropolis light transport algorithms are presented.One is based on improved acceptance probability.This algorithm combines the acceptance part to improve Metropolis light transport by using several acceptance probability functions to zoom the contribution to the scalar function of the acceptance probability function.The advantage is while satisfying the detail balance condition,it can also partly improve the acceptance probability of the proposed sample and reduces the random noise of the generated images.The experimental results show that there's less noise in the generated images of the improved algorithms compared with other algorithms while rendering the same image in the same time.The other is based on sampling distribution to improve the Metropolis light transport algorithm.This algorithm divides the sampling process into two stages,first periodically calculating the matrix similarity of the sampling number to decide the starting point of the second stage,and remembering the first stage's sampling distributed matrix to influence the second stage's mutation strategy.The second stage's mutation strategy proportionally samples the 8 neighboring pixels of the current sampling point.While satisfying the condition of the detail balance,it sets the ratio of the several sampling points' scalar contribution function as acceptance probability,which would solve the problem of plenty random noise in the complex illumination scenes.The experiment shows the improvedmethod could self-adaptively generate images with less noise in the same rendering time compared with other algorithms.The third method is the merged algorithm of the two methods,which are the improved Metropolis light transport algorithm based on acceptance probability and the improved Metropolis light transport algorithm based on self-adaptive sampling distribution respectively.This method utilize the coaction of the mutation strategy and the light path's selection to decrease the noise of the generated images.And the experimental results demonstrated that the merged algorithm can adaptively reduce the noise of the generated images in the indirect illimination scenes.At last I conclude the passage and put forward some future works depending on the analysis for some questions.