It has recently been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function that indicates the detected object’s location. We develop solutions based on impulse restoration for the Gaussian-noise case. We propose a new iterative approach, based on the expectation-maximization (EM) algorithm, that simultaneously estimates the background statistics and restores a delta function at the location of the template. We use a Monte Carlo study and localization-receiver-operating-characteristics curves to evaluate the performance of this approach quantitatively and compare it with existing methods. We present experimental results that demonstrate that impulse restoration is a powerful approach for detecting known objects in images severely degraded by noise. Our numerical experiments point out that the proposed EM-based approach is superior to all tested variants of the matched filter. This result demonstrates that accurate modeling and estimation of the background and noise statistics are crucial for realizing the full potential of impulse restoration-based template matching.
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