An Enhanced Edge Detection Using Laplacian Gaussian Filtering Method from Different Denoising Images
Keywords:
Edge detection, Laplacian of Gaussian, denoising, RMSE, PSNRAbstract
Image processing begins with the phase of edge detection (ED), which comes before the step of identification of objects and is regarded to be the foundation of the processing. This approach includes the process of identifying these spots from images from the viewpoint of the pixels where a quick shift in brightness occurs. Images may be categorized into a wide range of distinct types, including as those utilised in medical, satellite images, articular imaging, industrial imaging, general-purpose imaging, as well as more. By gathering information based on pictures, including the identification and translation of abnormal deflections, the main goal is to enhance clinical diagnosis. In this study, a fresh method for performing image denoising and edge detection preprocessing on various pictures will be given. Different photos will be gathered for this purpose from an internet source, and these will then be preprocessed using filtering techniques. The edges of the images that are entered will first be detected using a Laplacian gaussian filtering technique, after which noise may be removed using a Gaussian filtering strategy. For the purpose of demonstrating the efficacy of the proposed method, nine different photographs, one of which is an x-ray image, will be utilised in the demonstration process. The performance of the suggested edge detection technique will also be evaluated by computing the computational time metrics MSE, RMSE, and PSNR, also calculate execution time. Additionally, to verify the suggested methodology, a comparison with the traditional approaches will be done.
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