Performance Analysis of Adaptive Unsharp Masking Filter Techniques for Image Contrast Enhancement

Image contrast enhancement is known as one of the important techniques applied in the field of image processing. In order to improve the contrast of the captured image, different adaptive Unsharp Masking Filter (UMF) techniques were proposed by the researchers. In this paper, the main contribution is the implementation of three algorithms namely adaptive gain adjustment approach using an UMF (ASAUMF), design of UMF kernel and gain using Particle Swarm Optimization (UMFKG) and lastly, intensity and edge-based adaptive UMF (IntEdgUMF) which is denoted as Algorithm 1, 2 and 3 respectively. These algorithms were tested on the standard and biometric images like face images. This is because these adaptive UMF were mainly applied to natural scenery, but the importance of high image quality is not limited to the environment but also to the other fields such as biometric identification. Based on the results, Algorithm 1 is able to achieve the highest average PSNR values of 31.6079 dB and 35.8052 dB when applied on Set14 and LFW databases respectively. Although Algorithm 1 needs a longer running time in producing the output images, this algorithm can emphasize the details or information from the input image by enhancing the contrast of the image. Thus, Algorithm 1 can be concluded as the best adaptive UMF techniques among the three algorithms tested. For future work, the use of these adaptive UMF can be implemented onto various images, for instance gray scale images or other biometric images in order to test the effectiveness of the algorithms in different applications.