Maximum Entropy Direct Iterative Method for Image Restoration
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This paper focuses on restoring images blurred by horizontal uniform linear motion, investigating the application of Maximum Entropy Direct Iterative Method, Wiener Filtering, and Blind Restoration techniques. The study provides an in-depth analysis of these methods' underlying principles, advantages, and limitations, along with their practical effectiveness and applicable scenarios in image restoration. Implementation-wise, the Maximum Entropy Direct Iterative Method typically involves entropy maximization through iterative optimization algorithms (like gradient descent or Newton-Raphson), while Wiener Filtering employs frequency-domain filtering with noise statistics, and Blind Restoration often uses deconvolution algorithms without prior knowledge of the point spread function. Through this research, we gain deeper insights into blurred image restoration methodologies and clearer understanding of how to effectively apply these techniques to real-world problems.
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