摘要
The range variance of neighborhood filters is well estimated via distribution fitting of a chi scale mixtures model proposed in our previous work. However, it introduced computation overheads for deriving empirical distributions and performing iterative fitting. In this letter, we discuss how to greatly reduce the overheads for practical usage while maintaining denoising quality. For empirical distributions, a grid-subsampling strategy is adopted for acceleration. Regarding distribution fitting, two different methods are studied: equal-frequency merged distribution and L-moment fitting. The former reformulates the fitting process into entropy optimization for only few merged bins. It provides 6-13x speedup for model fitting with negligible quality loss and 9-20x speedup with ≤ 0.1dB PSNR drop by using 20 and 10 bins respectively. The latter performs table lookup of L-moments, instead of conventional moments, for robust fitting of heavy-tailed distributions. The fitting time then becomes negligible with ≤ 0.2 dB drop in most cases, e.g. the overall run time for bilateral 9 × 9$ filtering can be thus accelerated by around 6x. Experiments on bilateral and non-local means filters are also given to show the speedup, quality and robustness.