Standard Intensity Deviation Approach based Clipped Sub Image Histogram Equalization Algorithm for Image Enhancement

The limitations of the hardware and dynamic range of digital camera have created the demand for post processing software tool to improve image quality. Image enhancement is a technique that helps to improve finer details of the image. This paper presents a new algorithm for contrast enhancement, where the enhancement rate is controlled by clipped histogram approach, which uses standard intensity deviation. Here standard intensity deviation is used to divide and equalize the image histogram. The equalization processes is applied to sub images independently and combine them into one complete enhanced image. The conventional histogram equalization stretches the dynamic range which leads to a large gap between adjacent pixels that produces over enhancement problem. This drawback is overcome by defining standard intensity deviation value to split and equalize the histogram. The selection of suitable threshold value for clipping and splitting image, provides better enhancement over other methods. The simulation results show that proposed method out performs other conventional histogram equalization (HE) methods and effectively preserves entropy.