摘要: 大自然是灵感的源泉。许多算法都从大自然中汲取灵感，而算法灵感发展的来源也多种多样，质量也参差不齐。受自然启发的优化技术在图像处理领域发挥着至关重要的作用。它可以降低图像的噪声和模糊度，从而改善图像增强、图像分割和图像模式识别。图像增强是使图像能够用于特定应用的过程。图像质量与对比度相关，对比度提高会导致图像质量进一步下降。本文介绍了当前的均衡增强技术以及一些受自然启发的医学图像算法。此外，本文提出了一种图像增强方法，该方法结合了两种受自然启发的算法——粒子群优化 (PSO) 和蝙蝠优化算法 (BOA)，以实现更好的增强效果。本文使用了一种衡量图像增强效果的客观标准，该标准考虑了离散熵 (DE)、结构相似性指数矩阵 (SSIM) 和执行时间 (ET)。结果表明，与粒子群优化图像和现有的基于直方图的均衡化方法相比，蝙蝠算法增强图像的效果更佳，最终结果表明，所提图像增强方法不仅能提高图像的对比度，还能较好地保留图像的细节，具有良好的视觉效果。

Abstract: Nature is a very rich source of inspiration. Many algorithms have inspired from nature and source of algorithms inspiration development are diverse with different quality. Nature–inspired optimization techniques play an essential role in the field of image processing. It reduces the noise and blurring of images with improves the image enhancement, image segmentation, image pattern recognition. The Image enhancement is a process to make image ready for further uses in certain applications. The image quality is individually related with its contrast by rising the contrast, further disfigurements can be produced. In this paper covers current equalization enhancement technique some nature inspired algorithm for medical images. In addition, proposed an image enhancement method built by using two natures inspired algorithms Particle Swarm Optimization (PSO) and Bat Optimization Algorithms (BOA) combined to produce better enhancement. Here an objective criterion for measuring image enhancement is used which considers the Discrete Entropy (DE), the Structural Similarity Index Matrix (SSIM) and Executing Time (ET). The results showed the Bat Algorithm has produced a batter enhanced images when comparing with Particle Swarm Optimization images and the existing histogram-based equalization methods. The final results showed proposed image enhancement method can not only improve the contrast of the image, but also preserve the details of the image, which has a good visual effect.