Poisson noise reduction with non-local PCA
This addresses noise reduction for applications like spectral imaging and astronomy, but appears incremental as it builds on existing PCA and sparsity techniques.
The paper tackles denoising in photon-limited imaging, where Poisson noise causes artifacts, by introducing a method combining dictionary learning and sparse patch-based representations with PCA adapted for Poisson noise, showing it is highly competitive in very low light regimes.
Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denoising methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.