Image denoising for real image sensors

Date of Award


Degree Name

M.S. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Keigo Hirakawa


This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed in this work to calibrate the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we offer a new Poisson mixture image denoising scheme to overcome the problem. Experiments with real sensor data verify that the undersmooth is effectively improved.


Digital images Deconvolution, Image converters Design and construction, Image converters Calibration, Electrical Engineering, image denoising, image sensor, Poisson

Rights Statement

Copyright © 2015, author