Fast Non-blind Image Deblurring with Sparse Priors

Capturing clear images in dim light conditions remains a critical problem in digital photography. Long exposure time inevitably leads to motion blur due to camera shake. On the other hand, short exposure time with high gain yields sharp but noisy images. However, exploiting information from both the blurry and noisy images can produce superior results in image reconstruction. In this paper, we employ the image pairs to carry out a non-blind deconvolution and compare the performances of three different deconvolution methods, namely, Richardson Lucy algorithm, Algebraic deconvolution, and Basis Pursuit deconvolution. We show that the Basis Pursuit approach produces the best results in most cases.