Incoherent nonlinear deconvolution using an iterative algorithm for recovering limited-support images from blurred digital photographs
Joseph Rosen, Vijayakumar Anand
Abstract
Recovering original images from blurred images is a challenging task. We propose a new deconvolution method termed incoherent nonlinear deconvolution using an iterative algorithm (INDIA). Two inputs are introduced into the algorithm: one is a random or engineered point spread function of the scattering system, and the other is a blurred or distorted image of some object produced from this system. The two functions are Fourier transformed, and their phase distributions are processed independently of their magnitude. The algorithm yields the image of the original object with reduced blurring effects. The results of the new method are compared to two linear and two nonlinear algorithms under various types of blurs. The root mean square error and structural similarity between the original and recovered images are chosen as the comparison criteria between the five different algorithms. The simulation and experimental results confirm the superior performance of INDIA compared to the other tested deblurring methods.