Model-Based Restoration Procedure For Small, Low-Resolution Optical Images

John A. Saghri; Andrew G. Tescher

doi:10.1117/12.965941

17 March 1983 Model-Based Restoration Procedure For Small, Low-Resolution Optical Images

John A. Saghri, Andrew G. Tescher

Proceedings Volume 0359, Applications of Digital Image Processing IV; (1983) https://doi.org/10.1117/12.965941
Event: 26th Annual Technical Symposium, 1982, San Diego, United States

Abstract

A deterministic model-based restoration procedure is presented. The algorithm is effective for the restoration of coarsely sampled images degraded by diffraction and noise. The a priori information, an assumed parametric object model, is used to arrive at the solution. The object model is convolved with the optical system and sensing mechanism degradations and then matched against the limited number of available samples. The unknown parameters are then estimated using a numerical least mean square error optimization procedure. The method has been tested with a double delta function model via digital simulations. A zero-mean additive white Gaussian background noise process was assumed. The technique requires high signal-to-noise ratio (SNR) to resolve the doublet with a separation distance smaller than the detector width. With increasing separation, good restoration can be achieved at low SNR. The procedure is applicable to restore generalized objects.

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John A. Saghri and Andrew G. Tescher "Model-Based Restoration Procedure For Small, Low-Resolution Optical Images", Proc. SPIE 0359, Applications of Digital Image Processing IV, (17 March 1983); https://doi.org/10.1117/12.965941

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KEYWORDS

Sensors

Signal to noise ratio

Point spread functions

Digital image processing

Error analysis

Image processing

Model-based design

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