Low Light Image Denoising Based on Poisson Noise Model and Weighted TV Regularization

Abstract

Since the signal-to-noise ratio (SNR) is low in a dark environment, the captured images are seriously corrupted by sensor noise. Moreover, the sensor noise is signal-dependent, and has different characteristics from Gaussian distribution. In this paper, we propose low light image denoising based on Poisson noise model and weighted total variation (TV) regularization. In the data fidelity term, we adopt Poisson noise model to consider characteristics of sensor noise. In the regularization term, we present TV regularization based on two weights: Ratio between intensity mean and variance, and edge directionality. The ratio between intensity mean and variance adjusts the smoothing degree, while the edge directionality preserves image details. Experimental results demonstrate that the proposed method effectively removes sensor noise in low light images as well as outperforms the-state-of-the-arts in terms of the naturalness image quality evaluator (NIQE).