A new method for deconvolution of one-dimensional and multidimensional data is suggested. The proposed algorithm is local in the sense that the deconvolved data at a given point depend only on the value of the experimental data and their derivatives at the same point. In a regularized version of the algorithm the deconvolution is constructed iteratively with the help of an approximate deconvolution operator that requires only the low-order derivatives of the data and low-order integral moments of the point-spread function. This algorithm is expected to be particularly useful in applications in which only partial knowledge of the point-spread function is available. We tested and compared the proposed method with some of the popular deconvolution algorithms using simulated data with various levels of noise.
© 2003 Optical Society of America
(100.1830) Image processing : Deconvolution
(100.2980) Image processing : Image enhancement
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution
Timur E. Gureyev, Yakov I. Nesterets, Andrew W. Stevenson, and Stephen W. Wilkins, "A Method for Local Deconvolution," Appl. Opt. 42, 6488-6494 (2003)