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9 May 2011 A new blur kernel estimator and comparisons to state-of-the-art
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Abstract
This paper presents a simple, fast, and robust method to estimate the blur kernel model, support size, and its parameters directly from a blurry image. The edge profile method eliminates the need for searching the parameter space. In addition, this edge profile method is highly local and can provide a measure of asymmetry and spatial variation, which allows one to make an informed decision on whether to use a symmetric or asymmetric, spatially varying or non-varying blur kernel over an image. Furthermore, the edge profile method is relatively robust to image noise. We show how to utilize the concepts behind the statistical tools for fitting data distributions to analytically obtain an estimate of the blur kernel that incorporates blur from all sources, including factors inherent in the imaging system. Comparisons are presented of the deblurring results from this method to current common practices for real-world (VNIR, SWIR, MWIR, and active IR) imagery. The effect of image noise on this method is compared to the effect of noise on other methods.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Leslie N. Smith, James R. Waterman, and K. Peter Judd "A new blur kernel estimator and comparisons to state-of-the-art", Proc. SPIE 8014, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII, 80140N (9 May 2011); https://doi.org/10.1117/12.888126
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