<title>Quantitative evaluation of image enhancement algorithms</title>

Abstract

Functional evaluation of image processing algorithms is to determine how well they process images. The evaluation is, therefore, closely related to the assessment of image quality. The commonly used metrics are signal-to-noise ratio, mean square error, absolute error, and correlation. Unfortunately, these measures cannot adequately describe the visual quality of a processed image and, thence, may not properly evaluate the algorithms. This paper presents a new quantitative method for evaluating image enhancement (noise reduction) filters by proposing a new image quality metric. This approach is based on the psycho-visual study of noise sensitivity of human vision and a study of the performance of enhancement filters. The image quality metric is defined with respect to image intensity changes called the spatial activity. The functional evaluation of an enhancement filter uses this quality metric and includes quantitative measures of the filter's noise removal ability and the filter-caused distortion. It is shown that, to produce a good visual image quality, a filter should have both better noise reduction ability in low spatial activity regions and less distortion in high spatial activity regions.