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.
CITATION STYLE
Das, R., Bajpai, A., & Venkatesan, S. M. (2017). Fast non-blind image deblurring with sparse priors. In Advances in Intelligent Systems and Computing (Vol. 459 AISC, pp. 629–641). Springer Verlag. https://doi.org/10.1007/978-981-10-2104-6_56
Mendeley helps you to discover research relevant for your work.