Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ.
IEEE Trans Med Imaging. 1994;13(4):573-86. doi: 10.1109/42.363111.
Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE.
现有的图像对比度增强方法主要关注待处理图像的属性，而不考虑观察者的特征。在某些应用中，特别是在医学成像领域，通过包含某些基本的人类视觉特性，可以实现用于诊断目的的有效对比度增强。在这里，作者提出了一种新颖的自适应算法，该算法根据图像的局部对比度和观察者的“Just-Noticeable-Difference”（JND）来调整所需的对比度增强量。该算法始终在输出图像中产生足够的对比度，并且即使在尖锐过渡区域周围也几乎没有振铃伪像，这在传统对比度增强技术处理的图像中经常出现。通过分离图像的平滑区域和细节区域，并考虑噪声可见度对图像空间活动的依赖性，该算法对它们进行不同的处理，从而避免了对噪声的过度增强，这是许多现有对比度增强技术的另一个常见问题。目前的 JND 引导自适应对比度增强（JGACE）技术非常通用，可以应用于各种图像。特别是，它在数字射线照相应用中具有很大的优势，其目标是提高图像的诊断效用。详细的性能评估以及与现有技术的比较证明了 JGACE 的强大功能。
