Proceedings Article | 10 May 2011
Proc. SPIE. 8014, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII
KEYWORDS: Infrared imaging, Mid-IR, Short wave infrared radiation, Point spread functions, MATLAB, Statistical analysis, Cameras, Image quality, Deconvolution, Lawrencium
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.
