A Novel Approach for Image Enhancement Using Morphological Operators

Image Enhancement through De-noising is one of the most important applications of Digital Image Processing and is still a challenging problem. Images are often received in defective conditions due to usage of Poor image sensors, poor data acquisition process and transmission errors etc., which creates problems for the subsequent process to understand such images. The present paper gives the detail of various noise effects on the images and also discusses the methods to remove the noise by using Gaussian filter and to enhance the image quality using bilateral filtering method. The Experimental results performed on a set of standard test images for a wide range of noise corruption levels. The present paper also discusses the enhancement of the text images. The work is implemented on the MATLAB environment. The various results are shown in the simulation result section.

[1]  John C. Russ,et al.  The Image Processing Handbook , 2016, Microscopy and Microanalysis.

[2]  Rakhi C. Motwani,et al.  Survey of Image Denoising Techniques , 2004 .

[3]  Alper Pahsa.  MORPHOLOGICAL IMAGE PROCESSİNG WITH FUZZY LOGIC , 2006 .

[4]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[5]  Jean-Michel Morel,et al.  Nonlocal Image and Movie Denoising , 2008, International Journal of Computer Vision.

[6]  Frank Y. Shih,et al.  Image Processing and Mathematical Morphology: Fundamentals and Applications , 2017 .

[7]  Yan Huo,et al.  Research on image denoising methods based on wavelet transform and rolling-ball algorithm , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[8]  Jafar Ramadhan Mohammed,et al.  An Improved Median Filter Based on Efficient Noise Detection for High Quality Image Restoration , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[9]  Guillermo Sapiro,et al.  Fast image and video denoising via nonlocal means of similar neighborhoods , 2005, IEEE Signal Processing Letters.

[10]  Antonio F. Díaz,et al.  Parameter Configurations for Hole Extraction in Cellular Neural Networks (CNN) , 2002 .

[11]  S. Sivanandam,et al.  Introduction to Fuzzy Logic using MATLAB , 2006 .

[12]  Steven J. Simske,et al.  Image Denoising Through Support Vector Regression , 2007, 2007 IEEE International Conference on Image Processing.