Comparing our method with a state-of-the-art single image denoising [Dabov et al.] and an existing multi-view stereo denoising method [Vaish et al.] on synthetic Poisson noise.

In Matlab, NoisyImage = K*poissrnd(CleanImage/K), where poissrnd is a Poisson random number generator and K=38 for this image set. This operation simulates the process that incoming light is darkened by a factor of K, recorded by a photoreceptor, and then amplified by a factor of K before quatization. At the end, the mean of NoisyImage is CleanImage, and its variance is K*CleanImage.

In the rest of the figures, noisy input images are scaled down by a factor 1/2 to avoid saturating bright noisy pixels for illustration purpose.

Comparison between different denoising approaches

One of 25 noisy input images, PSNR=13.6957	Single image denoising [Dabov et al.], PSNR=24.758	25-view stereo denoising [Vaish et al.], PSNR=17.124	Our 25-view denoising, PSNR=27.699	Ground truth

Comparison between different denoising approaches (Insets)

Noisy patch	Dabov et al.	25-view stereo denoising [Vaish et al.]	Our result	Ground truth