Total Variation (TV) Denoising Algorithm with PDF Correction

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Resource Overview

This program implements a Total Variation (TV) denoising algorithm with PDF-based corrections to address minor issues in the standard TV approach. The algorithm effectively removes noise while preserving edge information in the original image. The implementation demonstrates how gradient-based minimization and edge-preserving regularization can be combined for improved image restoration, with key functions handling noise reduction through iterative optimization.

Detailed Documentation

In this program, we implement a Total Variation (TV) denoising algorithm and incorporate PDF-based corrections to resolve minor issues present in conventional TV methods. The algorithm effectively eliminates noise while maintaining the edge details of the original image. Our implementation involves minimizing a TV-based energy functional through gradient descent or similar optimization techniques, where the PDF adjustments help stabilize convergence and reduce artifacts. The core functionality includes regularization terms that penalize high-frequency noise without smoothing critical edges, using numerical methods such as finite differences for gradient computation. The objective is to deliver a more accurate and reliable denoising solution that meets user requirements for image quality, with the code structured to handle various noise types and image formats through configurable parameters.

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