Image denoising as a conditional expectation

TL;DR

This paper introduces a data-driven image denoising method that models the true image as a conditional expectation within a probabilistic framework, ensuring convergence and optimal parameter selection.

Contribution

It presents a novel interpretation of noisy images as samples from a probability space and recovers the true image as a conditional expectation using kernel integral operators in RKHS.

Findings

Method converges as pixel count increases.

Kernel-based approach provides unbiased denoising.

Parameters can be optimized for finite images.

Abstract

All techniques for denoising involve a notion of a true (noise-free) image, and a hypothesis space. The hypothesis space may reconstruct the image directly as a grayscale valued function, or indirectly by its Fourier or wavelet spectrum. Most common techniques estimate the true image as a projection to some subspace. We propose an interpretation of a noisy image as a collection of samples drawn from a certain probability space. Within this interpretation, projection based approaches are not guaranteed to be unbiased and convergent. We present a data-driven denoising method in which the true image is recovered as a conditional expectation. Although the probability space is unknown apriori, integrals on this space can be estimated by kernel integral operators. The true image is reformulated as the least squares solution to a linear equation in a reproducing kernel Hilbert space (RKHS), and involving various kernel integral operators as linear transforms. Assuming the true image to be a continuous function on a compact planar domain, the technique is shown to be convergent as the number of pixels goes to infinity. We also show that for a picture with finite number of pixels, the convergence result can be used to choose the various parameters for an optimum denoising result.