It is believed that the fusion of multiple different images into a single image should be of great benefit to Warfighters
engaged in a search task. As such, more research has focused on the improvement of algorithms designed for image
fusion. Many different fusion algorithms have already been developed; however, the majority of these algorithms have
not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of
this research is to apply a visual performance-based assessment methodology to assess four algorithms that are
specifically designed for fusion of multispectral digital images. The image fusion algorithms used in this study included
a Principle Component Analysis (PCA) based algorithm, a
Shift-invariant Wavelet transform algorithm, a Contrast-based
algorithm, and the standard method of fusion, pixel averaging. The methodology used has been developed to
acquire objective human visual performance data as a means of evaluating the image fusion algorithms. Standard
objective performance metrics, such as response time and error rate, were used to compare the fused images versus two
baseline conditions comprising each individual image used in the fused test images (an image from a visible sensor and
a thermal sensor). Observers completed a visual search task using a spatial-forced-choice paradigm. Observers
searched images for a target (a military vehicle) hidden among foliage and then indicated in which quadrant of the
screen the target was located. Response time and percent correct were measured for each observer. Results of this
study and future directions are discussed.
Future military imaging devices will have computational capabilities that will allow agile, real-time image enhancement. In preparing for such devices, numerous image enhancement algorithms should be studied. However, these algorithms need evaluating in terms of human visual performance using military-relevant imagery. Evaluating these algorithms through objective performance measures requires extensive time and resources. We investigated several subjective methodologies for down-selecting algorithms to be studied in future research. Degraded imagery was processed using six algorithms and then ranked along with the original non-degraded and degraded imagery through the method of paired comparisons and the method of magnitude estimation, in terms of subjective attitude. These rankings were then compared to objective performance measures: reaction times and errors in finding targets in the processed imagery. In general, we found associations between subjective and objective measures. This leads us to believe that subjective assessment may provide an easy and fast way for down-selecting algorithms but at the same time should not be used in place of objective performance-based measures.
While vast numbers of image enhancing algorithms have already been developed, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research was to apply a visual performance-based assessment methodology to evaluate six algorithms that were specifically designed to enhance the contrast of digital images. The image enhancing algorithms used in this study included three different histogram equalization algorithms, the Autolevels function, the Recursive Rational Filter
technique described in Marsi, Ramponi, and Carrato1 and the multiscale Retinex algorithm described in Rahman, Jobson and Woodell2. The methodology used in the assessment has been developed to acquire objective human visual performance data as a means of evaluating the contrast enhancement algorithms. Objective performance metrics, response time and error rate, were used to compare algorithm enhanced images versus two baseline conditions, original non-enhanced images and contrast-degraded images. Observers completed a visual search task using a spatial-forcedchoice paradigm. Observers searched images for a target (a military vehicle) hidden among foliage and then indicated in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Results of the study and future directions are discussed.
While vast numbers of image enhancing algorithms have already been developed, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research was to develop a visual performance-based assessment methodology and apply it to assess three Retinex algorithms. The image enhancing algorithms used in this study are the two algorithms described in Funt, Ciurea, and McCann as McCann99 Retinex and Frankle-McCann Retinex, and the multiscale Retinex with color restoration (MSRCR) algorithm. This paper discusses the methodology developed to acquire objective human visual performance data as a means of evaluating various image enhancement algorithms. The basic approach is to determine whether or not standard objective performance metrics, such as response time and error rate, are improved when viewing the enhanced images versus the baseline, non-enhanced images. Four observers completed a visual search task using a spatial-forced-choice paradigm. Observers had to search images for a target (a military vehicle) hidden among foliage and then indicate in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Future directions and the viability of this technique are also discussed.
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