Title :
A novel blind deconvolution scheme for image restoration using recursive filtering
Author :
Kundur, Deepa ; Hatzinakos, Dimitrios
Author_Institution :
Dept. of Electr. & Comput. Eng., Toronto Univ., Ont., Canada
fDate :
2/1/1998 12:00:00 AM
Abstract :
We present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function. The technique applies to situations in which the scene consists of a finite support object against a uniformly black, grey, or white background. This occurs in certain types of astronomical imaging, medical imaging, and one-dimensional (1-D) gamma ray spectra processing, among others. The only information required are the nonnegativity of the true image and the support size of the original object. The restoration procedure involves recursive filtering of the blurred image to minimize a convex cost function. We prove convexity of the cost function, establish sufficient conditions to guarantee a unique solution, and examine the performance of the technique in the presence of noise. The new approach is experimentally shown to be more reliable and to have faster convergence than existing nonparametric finite support blind deconvolution methods. For situations in which the exact object support is unknown, we propose a novel support-finding algorithm
Keywords :
convergence of numerical methods; deconvolution; filtering theory; image restoration; minimisation; noise; optical transfer function; 1D gamma ray spectra processing; astronomical imaging; blind deconvolution; blurred image; convergence; convex cost function minimisation; finite support object; grey background; image restoration; linearly degraded images; medical imaging; noise; nonparametric finite support; performance; point spread spectrum; recursive filtering; sufficient conditions; support size; support-finding algorithm; true image nonnegativity; uniformly black background; unique solution; white background; Biomedical imaging; Cost function; Deconvolution; Degradation; Filtering; Image restoration; Layout; Noise reduction; Optical imaging; Sufficient conditions;
Journal_Title :
Signal Processing, IEEE Transactions on