  Denoising is a fundamental imaging problem. Versatile but fast filtering has
been demanded for mobile camera systems. We present an approach to multiscale
filtering which allows real-time applications on low-powered devices. The key
idea is to learn a set of kernels that upscales, filters, and blends patches of
different scales guided by local structure analysis. This approach is trainable
so that learned filters are capable of treating diverse noise patterns and
artifacts. Experimental results show that the presented approach produces
comparable results to state-of-the-art algorithms while processing time is
orders of magnitude faster.
