Parallel Kalman Filter Bank Design for Adaptive Image Restoration
One of the basic problems in image reconstruction and restoration is to improve the visual quality of the degraded data of the image at hand. In many practical cases, the observed image is a degraded version of the ideal (original) image due to noise and blur. The problem which is solved here is that of finding an optimal estimate of the ideal image on the basis of the observed function that describes the degraded image and a specific optimality criterion. The image is modelled by a linear state space model involving space invariant additive Gaussian white noise. The adaptive image restoration is performed using a parallel bank of filters (partitioning approach) for estimating the state of the image model when the covariance function of the observed image has a separable exponential form depending on an unknown parameter “a”. The method has so far been tested with simulated images.
KeywordsKalman Filter Point Spread Function State Space Model Image Model Image Restoration
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