Comparison of Edge Detection of Images with Various Types of Noise and Denoising Methods

In this paper, we show comparison results of edge detection of images that have additive gaussian noise or salt and pepper noise by using various techniques of noise removal such as filtering, morphology and deep learning based ones. In particular, this present work provides comparison results of noise removal by using gaussian filter, open and close operations of morphology and auto-encoder model followed by carrying out edge detection. Robert cross, Sobel, Prewitt and Canny detectors are used for edge detection of the images with noise removal. Experimental results show that noise removal results are different with characteristics of noise and techniques applied for noise removal. In addition, deep learning based technique, auto-encoder does not always shows superior results of noise removal, particularly in the case of existence of salt-pepper noise. In the experiments, gaussian noise or salt-pepper noise is used and peak signal noise ratio (PSNR) is used for quantitative comparison and the results of edge detection is qualitatively compared from visual perspective.