Comparing Image Segmentation Algorithms

TL;DR

This paper introduces a simulated annealing-based method for denoising binary images, demonstrating superior performance over traditional methods by effectively removing noise and preserving details.

Contribution

It presents a novel global optimization approach combining simulated annealing with localized strategies for binary image denoising.

Findings

Achieves 99.19% agreement with original images

Outperforms ICM with higher restoration accuracy

Effectively preserves structural details during denoising

Abstract

This paper presents a novel approach for denoising binary images using simulated annealing (SA), a global optimization technique that addresses the inherent challenges of non convex energy functions. Binary images are often corrupted by noise, necessitating effective restoration methods. We propose an energy function E(x, y) that captures the relationship between the noisy image y and the desired clean image x. Our algorithm combines simulated annealing with a localized optimization strategy to efficiently navigate the solution space, minimizing the energy function while maintaining computational efficiency. We evaluate the performance of the proposed method against traditional iterative conditional modes (ICM), employing a binary image with 10% pixel corruption as a test case. Experimental results demonstrate that the simulated annealing method achieves a significant restoration improvement, yielding a 99.19% agreement with the original image compared to 96.21% for ICM. Visual assessments reveal that simulated annealing effectively removes noise while preserving structural details, making it a promising approach for binary image denoising. This work contributes to the field of image processing by highlighting the advantages of incorporating global optimization techniques in restoration tasks.