一种新的基于Loermgan算法的聚类图像绘制模型

Ishan Sharma, Yongwei Nie
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引用次数: 0

摘要

利用已知部分的信息从图像中恢复受损或缺失区域的过程称为图像修复。为了将损坏的图像修复成与真实图像相似的新图像,迄今为止已经建立了许多复杂的方法。然而,在图像缺失区域较大的情况下,这些模型不能有效地解决问题。同样,这些方法在边缘是无效的。因此,本文利用对数指数规则生成对抗网络算法,提出了一种新的以聚类为中心的图像绘制系统。首先,执行两个重要步骤,即(i)去噪和(ii)对比度增强(CE),对输入图像进行预处理。然后利用自适应最大单侧框滤波(AMOSBF)算法,很好地保留了预处理后图像的边缘。然后,从边缘保留图像中提取最需要的特征。其次,采用SDFDPCA (supermum Distance Fast Density Peaks Clustering Algorithm)算法对提取的特征进行聚类。接下来,该模型被称为指数对数规则生成对抗网络(LOERMGAN),该模型可以有效地重建实际图像,并将聚类特征和掩码图像以及掩码本身馈馈化。在本研究中,使用了可公开访问的数据集ADE20k、Paris和Places2。随后,用流行的方法类比得到的结果。实验结果表明,所提出的模型通过有效地重建图像胜过其他流行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Clustering-Based Image Inpainting Model Using the Loermgan Algorithm
The process of recovering the damaged or missing areas from an image utilizing information as of known portions is termed Image Inpainting. To refurbish the damaged image into a new one alike an actual image, numerous sophisticated methodologies have been established to date. Nevertheless, in the case of images with a larger missing region, these models are not effective in addressing the problem. Likewise, these methodologies are ineffective towards the edge. Therefore, by utilizing the Log of Exponent Rule Generative Adversarial Network algorithm, a novel clustering-centric image inpainting system has been proposed here. Initially, two significant steps, namely (i) noise removal, and (ii) Contrast Enhancement (CE), are performed to pre-process the input images. After that, by utilizing the Adaptive Max One-Sided Box Filter (AMOSBF) algorithm, the pre-processed images’ edges are well-preserved. Then, the most needed features are extracted as of the edge preserved images. Next, by employing Supremum Distance Fast Density Peaks Clustering Algorithm (SDFDPCA), the features being extracted are clustered. Next, the proposed model, termed Log of Exponent Rule Mish Generative Adversarial Network (LOERMGAN), which reconstructs the actual images effectively, is fed with the clustered features and also the masked image and the mask itself. In this research, the openly accessible datasets termed ADE20k, Paris, and Places2 are utilized. Subsequently, the outcomes obtained are analogized with the prevailing methodologies. The experiential outcomes displayed that the proposed model outshines the other prevailing methodologies by effectively reconstructing images.
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