Background/Objectives: Due to the huge volume of data involved, it is very much challenging to design efficient contrast enhancement algorithms in real time applications. In this paper an efficient hardware is presented for image enhancement. Methods/Statistical Analysis: The computation algorithms are based on the calculations of image Probability Density Function (PDF) and Cumulative Distribution Function (CDF). For better results weighted PDF and smoothed CDF computations are performed. Then the adaptive gamma correction is used for enhancing the image contrast. A compensated CDF is used as the adaptive gamma parameter. To reduce hardware complexity, approximation techniques are employed. In the modified algorithm, the bi-histogram equalization is utilized. Xilinx system generator is used for hardware co-simulation. The hardware is implemented on an FPGA based 'Zed Board'. Findings: The hardware oriented method achieves similar quality image as the software approach and the results are qualitatively and quantitatively analyzed. The PDF and CDF based computations are faster than other image processing methods. So this algorithm is suitable for real time applications. The image is found to have a better quality in the modified AGCWD method. The PSNR value also is found to be better than the normal method. But the hardware utilization of the modified algorithms is found to be higher than the normal algorithm. The bi-histogram approach is suitable to preserve the mean brightness of the original image. Applications/Improvements: Future works may modify the proposed method for reducing the hardware requirements. Contrast enhancement is one of the crucial image processing techniques in high definition image and video applications. Image enhancement techniques find applications in LED and LCD display processing, medical image analysis etc.