A novel joint histogram equalization based image contrast enhancement

The limitation to the most commonly used histogram equalization (HE) technique is the inconsideration of the neighborhood info near each pixel for contrast enhancement. This gives rise to noise in the output image. To overcome this effect, a novel joint histogram equalization (JHE) based technique is suggested. The focus is to utilize the information among each pixel and its neighbors, which improves the contrast of an image. The suggested method is developed in a truly two-dimensional domain. The joint histogram is constructed using the original image and its average image. Further, it does not require a target uniform distribution for generating the output. The two-dimensional cumulative distribution function (CDF) is utilized as a mapping function to get the output pixel intensity. Extensive experiments are performed using 300 test images from BSD database. The experimental analysis indicates that the procedure produces better results than the state-of-the-art HE based contrast enhancement algorithms. More significantly, it produces the best results even for images having a narrow dynamic range. The implementation simplicity of the proposed algorithm may attract researchers to explore the idea for new applications in image processing.