Adaptive Deblurring of Noisy Images

An algorithm for adaptive deblurring of images has been designed to be less adversely affected by image noise than are prior deblurring algorithms. The need for this or another noise-tolerant deblurring algorithm arises as follows: For a typical imaging instrument, in which the blurring function (also known as the point-spread function) approximates a Gaussian function in the spatial-frequency domain, a simplistic spatial-frequency-domain deblurring function equal to the inverse of the blurring function magnifies the noise at high spatial frequencies. In the present adaptive deblurring algorithm, the spatial-frequency-domain deblurring function is the product of (1) the inverse of spatial-frequency-domain blurring function and (2) a smoothing or low-pass filter function denoted variously as a power window or a P-deblurring filter function (wherein “P” signifies “power”). The term “adaptive” in the name of the algorithm characterizes the process for choosing the parameters of the power window.