Blur Identification and Correction for a given Imaging System


Estimating the blur function is the first step in all the image restoration techniques. A priori knowledge of the blur phenomenon helps improve the quality and certainty of image restoration. In this paper, a method of estimating the blur function for imaging setups that are not subject to change, for example surveillance cameras, is developed using the Wiener filter. A random noise image is used as a test image. Deconvolution is performed between the original test image and its observation taken from the given imaging system, to obtain the blur estimate for the system. Necessary preprocessing steps are implemented to compensate for the non-ideal nature of the imaging environment. Blur correction is then implemented on different observations taken from the same imaging system. The performance of the restoration is evaluated using Mean Square Error and Signal-to-Noise Ratio Improvement criteria. 1. DEGRADATION OF IMAGES This paper deals with the identification of a blur function for an imaging system and blur correction by inverse filtering. The images obtained by various imaging systems are often degraded and are unsatisfactory to reveal all of the information present. Imperfections in the optical system, substandard imaging environment, and optical as well as electrical noise are the main causes of these degradations. Figure 1.1 shows a block diagram of a common electronic imaging system. Figure 1.1: Image Degradation Model The imaging system introduces distortions at different stages, starting from the optical components up to the electronic data conversions and recording of the images. The mathematical model for linear degradation caused by blurring and additive noise in a typical imaging system is given by (1.1) as stated below. y i j h i j k l f k l n i j l N k M ( , ) ( , ; , ) ( , ) ( , ) = + = = ∑ ∑ 1 1

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@inproceedings{Chitale1999BlurIA, title={Blur Identification and Correction for a given Imaging System}, author={Sucheta Chitale and Rose Hulman}, year={1999} }