Successful design of a !at-pro"le multiplexed optical imaging system requires the use of adaptive techniques to make intelligent resource allocation based on the information content in the imaging systemDs "eld-of-view. This paper explores techniques for "nding regions of interest in aerial images using local entropy as a descriptor. A novel method for identifying regions-of-interest in images is developed using the 2D normalized power spectral density within GillesD saliency map estimator. Application of the method to candidate aerial images shows its ability to identify consistent regions of interest for such data for varying block sizes and under additive noise.
!"#$% &$'()-computational imaging systems, information theory, saliency, image reconstruction
Using intelligent resource allocation based on the information content in the imaging systemGs !eld-of-view for the successful design of a "at-pro!le multiplexed optical imaging system requires the use of adaptive techniques. This paper describes a model-based technique for determining regions of interest in aerial images using the 2D normalized power spectral density within GillesG saliency map estimator. The proposed technique exploits the 1/f α spatial spectral shape of such natural imagery in a computationally-simple approach that is robust to additive noise. Application of the method to candidate aerial images shows its ability to identify consistent regions of interest for such data.
Macroscopic imagers are subject to constraints imposed by the wave nature of light and the geometry of image formation. The former limits the resolving power while the latter results in a loss of absolute size and shape information. The suite of methods outlined in this work enables macroscopic imagers the unique ability to capture unresolved spatial detail while recovering topographic information. The common thread connecting these methods is the notion of imaging under patterned illumination. The notion is advanced further to develop computational imagers with resolving power that is decoupled from the constraints imposed by the collection optics and the image sensor. These imagers additionally feature support for multiscale reconstruction.
Critical examination of the slanted-edge method for color SFR measurement reveals inaccuracies in the estimated SFR, due to the use of demosaicing. The proposed method resolves these inaccuracies by eliminating the need for demosaicing during SFR measurement. OCIS codes: (110.4100) Modulation transfer function; (110.4850) Optical transfer functions
IntroductionThe spatial frequency response (SFR) of a digital image acquisition system is an objective measure of image quality that describes an imaging system's ability to capture or maintain the relative radiometric contrast of increasingly fine sinusoidal patterns [1]. The SFR neatly encapsulates the influence of the optical elements, the pixel MTF and the camera electronics, on image quality.The slanted-edge algorithm outlined in the ISO12233 standard is the most celebrated method for identifying the SFR, and finds widespread use in diverse disciplines such as remote sensing and radiology. The benefit of the slanted-edge method over its counterparts lies in the relative ease with which the SFR can be determined for aliased imaging systems. The method relies on the analysis of the sampled image of a slanted edge. It exploits the variation in the sampling phase of the slanted edge to create a "super-resolved" edge response, whose resolution exceeds the sensor's native resolution.Our investigations into the slanted-edge algorithm and tools based on the algorithm have revealed an interesting fact: demosaicing 1 influences the SFR estimates of color cameras. The SFR plots of Fig.1 illustrate this behavior.
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