Evaluation of a New Adaptive Contrast Enhancement Algorithm Based on Local Standard Deviation
对一种新的基于局部标准差的自适应对比度增强算法的评价

Local standard deviation (LSD) is one of the effective ways to describe detail contrast. An adaptive contrast enhancement (ACE) algorithm is introduced and evaluated, in which the contrast gain is determined by mapping the local standard deviation histogram of an image to a Gaussian distribution function. The contrast gain is nonlinearly adjusted to have large values for pixels in detail regions, and small values for those in smooth regions and regions of sharp edges, so that noise overenhancement and ringing artifacts can be reduced while improving the detail contrast with less computational burden. The performance of this algorithm is illustrated with different types of images, evaluated by means of contrast-to-noise ratio (CNR) and compared with other algorithms.