Multicycle non-local means denoising of cardiac image sequences

Abstract

Non-local means (NLM) noise reduction is effective yet computationally intensive, since each output pixel is a weighted average of all input pixels. Most implementations therefore restrict the scope of the average to a smaller neighborhood around each pixel, which limits the method's full potential. Here we propose to apply NLM to reduce the noise in fluorescence microscopy image sequences of the beating heart, whose quasi-repeatable pattern produces multiple realizations of similar image patches in different heart beats. We propose to restrict the averaging from the entire sequence to a subset of globally similar frames across multiple heart beats. Using a high-SNR brightfield microscopy image sequence of a beating embryonic zebrafish heart that we artificially corrupt with noise, we illustrate the benefits of selecting non-adjacent frames rather than the immediate temporal neighborhood in the vicinity of the pixel being denoised. The image quality of our NLM approach is also better than that obtained by directly computing the sample median of matching frames over multiple heartbeats, a commonly used method. Finally, we demonstrate the applicability of our proposed scheme to low-intensity fluorescence images of the embryonic zebrafish heart.