✍ Scribed by Jia-Guu Leu
No coin nor oath required. For personal study only.
Histogram modification can improve the contrast of an image. Histogram equalization has been the most popular histogram modification technique. However, the technique has the tendency to magnify local noise for images with large homogeneous regions. In this paper we suggest a new histogram modification technique which utilizes the intensity distribution of the edge pixels of an image. We first identify the edge pixels of an image. Then the intensity histogram of the edge pixels is constructed. An intensity transformation function is derived from the edge-pixel histogram and then applied to the entire image. In general, this transformation will increase the intensity difference between neighboring homogeneous regions. We also have suggested three tools to measure the performance of contrast-enhancing methods. The three measurements are image contrast value, image information loss value, and local intensity variance value. Our goal for enhancing is to significantly increase an image's contrast value while keeping both the information loss value and the local intensity variance value low. In the experiments, we have compared the performance of the suggested method with that of the ordinary histogram equalization technique and the local area histogram equalization (LAHE) technique using both synthetic and real images. The results were then evaluated by the three tools. The suggested method performed very well both analytically and visually.
e 1992
Academic Press, Inc.
the full available intensity range 131. Since it only concerns the intensity span of the original histogram and does not take into consideration the distribution of the intensity values, this technique, in general, has only limited enhancing capability.
The histogram specification technique reshapes the histogram of an image into a certain prescribed shape 14, 51. For this, one needs to know what the correct histogram for a given image should be like. Therefore, this method requires a priori knowledge about the scene.
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