Image Restoration with Different Noise Types
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Detailed Documentation
This documentation focuses on image restoration procedures. Specifically, we examine how to introduce different types of noise into given images and employ technical methods to restore them. We analyze the impact of various noise types on the image restoration process and discuss how to select optimal restoration methods to minimize errors and distortions. The implementation typically involves using MATLAB's imnoise() function to add Gaussian, salt-and-pepper, or Poisson noise, followed by restoration algorithms like Wiener filtering or median filtering. Additionally, we introduce cutting-edge image restoration techniques such as deep learning-based approaches using convolutional neural networks (CNNs), along with their practical applications in different scenarios. Through this document, readers will gain deeper insights into the principles and methodologies of image restoration, enabling more effective implementation of these techniques in practical applications.
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