These results illustrate the process of non-blind deconvolution on synthetically blurred images created from a combination of eight unblurred test images and eight blur kernels, described in the paper. As the individual images are small, results for all eight images are shown together for each kernel, along with the kernel itself. There are two versions of the synthetic images for each combination, differing in how much Poisson noise is added: S = 100 is less noisy and S = 10 is more noisy.

λ is the regularization parameter; higher values give more importance to the prior. The sparse prior refers to the hyper-Laplacian distribution for image gradients with parameter α = 0.8. The AR priors assume that the image gradients are correlated (according to a simple independent 2-D auto-regressive model with correlation parameters 0.3 and 0.6). The IID prior assumes they are independent. Results using the classical Richardson-Lucy algorithm (after 25 iterations) is also shown for comparion.