Comparing Image Segmentation Algorithms
Milind Cherukuri
TL;DR
The paper tackles binary image denoising under 10% random corruption by comparing Iterated Conditional Modes (ICM) and Simulated Annealing (SA) within a Markov random field framework. The authors formulate the energy as $E(\\mathbf{x},\\mathbf{y}) = h \\sum_i x_i - \\beta \\sum_{\\{i,j\\}} x_i x_j - \\eta \\sum_i x_i y_i$ and define $p(\\mathbf{x},\\mathbf{y}) = \\(1/Z\\) \\exp(-E(\\mathbf{x},\\mathbf{y}))$, seeking $\\mathbf{x}=\\arg\\min E(\\mathbf{x},\\mathbf{y})$. While ICM quickly converges to local optima, SA with a temperature schedule and local energy updates finds a near-global optimum, achieving $99.19\\%$ pixel agreement with the original image compared to $96.21\\%$ for ICM. The study demonstrates the benefit of global optimization for binary image restoration and discusses limitations due to the 4-adjacency prior and stochasticity, outlining future work to improve edge preservation and contextual modeling. The results have implications for image processing tasks requiring reliable denoising, including document analysis and medical imaging.
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.