Robustness Comparison of Traditional and Deep Learning-Based Image Segmentation Models on Noisy Images
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Robustness Comparison of Traditional and Deep Learning-Based Image Segmentation Models on Noisy Images
Authors:
Dharmendra Bhadauria
School of Computer Science and Application Galgotias University,
Greater Noida, UP dharmendrabhadauria77@gmail.com
Arun Bhardwaj
School of Computer Science and Application Galgotias university,
Greater Noida, UP arunbhardwaj12p@gmail.com
Kalyani Singh
School of Computer Science & Engineering Galgotias University,
Greater Noida, UP kalyani.singh@galgotiasuniversity.edu.in
S M Aqdas Hashmi
School of Computer Science and Application Galgotias University
Greater Noida, UP aqdas.hashmii@gmail.com
Abstract— Detecting objects in images with precision becomes increasingly difficult when the input is degraded by noise or distortion, as is common in real-world applications. This study investigates and compares the performance of a traditional edge detection technique (Canny) and a deep learning-based model (U- Net) for binary image segmentation under noisy conditions. We simulate three types of image noise—Gaussian, Salt & Pepper, and Motion Blur—on a public dataset and evaluate both models using Dice Score, Intersection over Union (IoU), and Structural Similarity Index (SSIM) to assess their quantitative accuracy and perceptual consistency. While U-Net demonstrates stronger resilience in capturing complex structures and maintaining segmentation accuracy, Canny proves more computationally efficient and surprisingly stable under certain distortions. Our results highlight the importance of selecting segmentation methods based not only on accuracy but also on noise robustness and deployment constraints. This work offers practical insights into the trade-offs between traditional and deep learning models for vision tasks in noisy environments.
Keywords— Image Segmentation, U-Net, Canny Edge Detection, Noise Robustness, Dice Score, IoU, SSIM, Deep Learning, Computer Vision
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