为了解决Metropolis光线追踪算法随机采样分布的稀疏性导致的生成图像随机噪声问题,提出了一种基于采样分布的突变策略.改进方法将采样过程分为两个阶段对Metropolis光线追踪算法进行改进,通过一阶段采样分布矩阵对二阶段突变策略进行影响,在当前采样点的8邻域像素位置进行小范围采样.在满足细节平衡条件的同时,设置多个采样点的标量贡献函数平均值比值为接收概率.结果表明,在相同渲染时间下改进算法生成的图像较原方法噪声更少,与目前较好的改进算法效果相当.通过改进突变策略的方式解决了间接光照场景中噪声较大的问题.%In order to solve the random noise problem in the image generation process caused by the sparsity of random sampling distribution in Metropolis light tracking ( MLT) algorithm, a mutation strategy based on sampling distribution was proposed. The sampling process was divided into two stages to improve the MLT algorithm, and the mutation strategy in the second stage was affected by the sampling distribution matrix generated from the first stage. In addition, a small scale of sampling was performed at the positions of eight-neighbor pixels of current sampling point. While satisfying the detailed balance condition, the average ratio of scalar contribution function of multiple sample points was set as the acceptance probability. The results show that the improved algorithm can generate smaller noise in the image compared with the original method at the same time, and the effect is equivalent to the better improved algorithms. The problem of big noise in the indirect illumination scene can be solved through improving the mutation strategy.
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