用模拟退火算法提升二值图像去噪效果，精度达99.19%。
Comparing Image Segmentation Algorithms
- 结合模拟退火与局部优化，全局搜索更优解
- 在10%噪声下准确率99.19%，优于ICM的96.21%
- 适合需要高保真度的图像修复场景
本文提出一种基于模拟退火（SA）的二值图像去噪新方法，针对非凸能量函数的优化难题。通过构建描述噪声图像y与理想清洁图像x关系的能量函数E(x, y)，结合模拟退火与局部优化策略，在保持计算效率的同时有效探索解空间。在10%像素被污染的测试图像上评估，该方法相比传统迭代条件模式（ICM）显著提升恢复效果：与原始图像的匹配率达99.19%，高于ICM的96.21%。视觉对比显示，该方法能有效去除噪声并保留结构细节，验证了全局优化在图像复原中的优势。本研究为图像处理领域提供了新的思路。
原文摘要 · Abstract (English)
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.
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