Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Unsharp Mask Filtering Technique over Histogram Equalization Technique

Abstract

Aim: The goal of study in this image enhancement technique is to enhance both contrast and sharpness of an image simultaneously to improve PSNR. Materials and Methods: Both unsharp mask filter and novel histogram equalization techniques were implemented on lung images which were collected from kaggle software. Samples were considered as (N=30) for unsharp mask filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical. com. As a result the total number of samples was calculated as 60. Matlab coding was written for extracting PSNR values of each image. Comparison and analysis has been made through SPSS software. Results: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than unsharp mask filtering technique. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and unsharp mask filtering. The novel histogram equalization technique showed higher results of PSNR (67.2860dB) with (p=0.04) in comparison with unsharp mask filtering (37.9313dB). Conclusion: Within this research study histogram equalization image enhancement technique has greater PSNR value of lung images than unsharp mask filtering technique.