Image enhancement has been paid great attention in several areas, and various image enhancement approaches are introduced by the researchers to enhance the images clearer from the degraded images. Accordingly, this paper presents the proposed ACSO-based IE-CGAN for image enhancement. Initially, an effective noisy pixel identification based on image enhancement is developed, which employs circular-based searching for improving the input image from several noises, like salt and pepper noise, speckle (impulse) noise, Gaussian noise, and random noise. Once the noisy pixels are identified, the pixel enhancement is done using the circular-based search. The noise removal is done using a statistical model, where the best threshold value is determining using the proposed Ant Cuckoo Search Optimization (ACSO) algorithm. The ACSO algorithm is newly designed by integrating Ant Lion Optimization (ALO) and the Cuckoo Search Optimization (CSO) algorithm. After that, the contrast enhancement is carried out using Image Enhancement Conditional Generative Adversarial Network (IE-CGAN), which is trained by the proposed ASCO algorithm. The experimentation is carried out using an osteoporotic vertebral fracture dataset, and the performance of image enhancement using ACSO-based IE-CGAN is evaluated based on Peak Signal to Noise ratio (PSNR), Structural Similarity Index (SSIM), and Second Derivative like Measure of Enhancement (SDME). The developed method achieves the maximal PSNR of 33.50 dB with salt and pepper noise, maximal SDME 41.38 dB with random noise, and maximal SSIM of 0.967 with salt and pepper noise.

中文翻译：

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基于 Ant Cuckoo 搜索优化的深度学习分类器用于脊髓图像中的图像增强

图像增强在多个领域受到了极大的关注，研究人员引入了各种图像增强方法，以从退化的图像中更清晰地增强图像。因此，本文提出了用于图像增强的基于 ACSO 的 IE-CGAN。最初，开发了一种基于图像增强的有效噪声像素识别，它采用基于圆形的搜索来改善输入图像中的几种噪声，如椒盐噪声、斑点（脉冲）噪声、高斯噪声和随机噪声。一旦识别出有噪声的像素，就使用基于圆形的搜索来完成像素增强。噪声去除是使用统计模型完成的，其中最佳阈值是使用建议的 Ant Cuckoo 搜索优化 (ACSO) 算法确定的。ACSO 算法是通过集成 Ant Lion Optimization (ALO) 和 Cuckoo Search Optimization (CSO) 算法而全新设计的。之后，使用由所提出的 ASCO 算法训练的图像增强条件生成对抗网络 (IE-CGAN) 进行对比度增强。使用骨质疏松性椎体骨折数据集进行实验，并基于峰值信噪比 (PSNR)、结构相似性指数 (SSIM) 和二阶导数等指标评估使用基于 ACSO 的 IE-CGAN 的图像增强性能增强 (SDME)。所开发的方法在椒盐噪声下的最大 PSNR 为 33.50 dB，在随机噪声下的最大 SDME 为 41.38 dB，在椒盐噪声下的最大 SSIM 为 0.967。