In this paper we propose Pixel-based Random Parameter Filtering (P-RPF) for effciently denoising images generated from complex illuminations with a high sample count. We design various operations of our method to have time complexity that is independent from the number of samples per pixel. We compute feature weights by measuring the functional relationships between MC inputs and output in a sample basis. To accelerate this sample-basis process we propose to use an upsampling method for feature weights. We have applied our method to a wide variety of models with different rendering effects. Our method runs signi?cantly faster than the original RPF, while maintaining visually pleasing and numerically similar results. Furthermore the performance gap between our method and RPF increases as we have more samples per pixel. As a result, our method shows more visually pleasing and numerically better results of RPF in an equal-time comparison.