Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible connectivity. As a result, overfitting and overlearning phenomena can easily emerge during the image inpainting procedure. Image inpainting refers to the repairing of missing parts of an image, given an image that is broken or incomplete. After the repairing operation is complete, there are obvious signs of repair in damaged areas, semantic discontinuities, and unclearness. This paper proposes an improved image inpainting method based on a new encoder combined with a context loss function. In order to obtain clear repaired images and ensure that the semantic features of images are fully learned, a generative network based on the fusion model of squeeze-and-excitation networks deep residual learning has been proposed to improve the application of network features in order to obtain clear images and reduce network parameters. At the same time, a discriminative network based on the squeeze-and-excitation residual Network has been proposed to strengthen the capability of the discriminative network. In order to make the generated image more realistic, so that the restored image will be more similar to the original image, a joint context-awareness loss training method (contextual perception loss network) has also been proposed to generate the similarity of the local features of the network constraint, with the result that the repaired image is closer to the original picture and more realistic. The experimental results can demonstrate that the proposed algorithm demonstrates better adaptive capability than the comparison algorithms on a number of image categories. In addition, the processing results of the image inpainting procedure were also superior to those of five state-of-the-art algorithms.

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基于编码器和相似性约束的改进图像修复算法

现有的基于神经网络模型的图像修复算法受到可见连接上的结构失真和模糊纹理的影响。因此，在图像修复过程中很容易出现过度拟合和过度学习的现象。图像修复是指在图像损坏或不完整的情况下修复图像的缺失部分。修复操作完成后，受损区域有明显修复痕迹，语义不连续，不清晰。本文提出了一种基于新编码器结合上下文损失函数的改进图像修复方法。为了获得清晰的修复图像并确保图像的语义特征被充分学习，提出了一种基于squeeze-and-excitation网络深度残差学习融合模型的生成网络，以提高网络特征的应用，以获得清晰的图像并减少网络参数。同时，提出了一种基于挤压激励残差网络的判别网络，以加强判别网络的能力。为了使生成的图像更加逼真，使还原后的图像与原始图像更加相似，还提出了一种联合上下文感知损失训练方法（上下文感知损失网络）来生成局部特征的相似性网络约束，使得修复后的图像更接近原图，更逼真。实验结果表明，所提出的算法在多个图像类别上表现出比比较算法更好的自适应能力。此外，图像修复程序的处理结果也优于五种最先进的算法。