Tree-Based Locally Linear Regression for Image Denoising

We present a new patch-based approach for image denoising that combines similar patches in the same image and from a set of training images. The key idea of our method is that we can partition the training samples according to the clean patches and efficiently learn a denoising operator for each partition. Given a noisy patch, we use self-similarity to compute an initial denoising result which is used to locate the relevant partitions. We apply the corresponding learned denoising operator to the original noisy patch. Our method does not suffer either from the blurring effect that commonly exists in self-similarity based methods or from the training size problem that is associated with training-based methods. We evaluate our method on three benchmark datasets as well as real mobile images. Experimental results show that our approach consistently outperforms BM3D in terms of both peak signal-to-noise ratio and visual quality.