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Poisson Noise Removal for Image Demosaicing

With increasing resolution of the sensors in camera detector arrays, acquired images are ever more susceptible to perturbations that appear as grainy artifacts called ‘noise’. In real acquisitions, the dominant noise model has been shown to follow the Poisson distribution, which is signal dependent. Most color image cameras today acquire only one out of the R, G, B values per pixel by means of a color filter array in the hardware, and in-built software routines have to undertake the task of obtaining the rest of the color information at each pixel through a process termed demosaicing. The presence of the Poisson noise can significantly degrade the output of a demosaicing algorithm. In this paper, we propose and compare two dictionary learning methods to remove the Poisson noise from the single channel images by directly solving a Poisson likelihood problem or performing a variance stabilizer transform prior to demosaicing. Experimental results on simulated noisy images as well as real camera acquisitions, show the advantage of these methods over approaches that remove noise subsequent to demosaicing.