Image restoration is a technique that utilizes the edge of a corrupted area. The information content of the damaged information area is inferred based on the remaining information of these images, and then the damaged area is filled to achieve image restoration. To solve the problem of image occlusion in practical applications, a squeeze-excitation network-deep convolution generative adversarial network (SE-DCGAN) was proposed. First, many new sharp images are generated using SE-DCGAN. Then, in the generated image, the most similar image is found based on the context semantics of the original image and the encoding of the unfilled portion to fill the original image. SE-DCGAN introduces maxout activation with powerful fitting capabilities to improve image generation efficiency and avoid image generation redundancy. Experiments based on three datasets of CelebA, Street View House Number and anime avatars, showed that our method successfully predicted a large number of missing regions. This method improves the recognition rate of occluded images, produces high-quality perceptual results, and is flexible enough to handle a variety of masks or obstructions.

图像恢复是一种利用损坏区域边缘的技术。根据这些图像的剩余信息推断出受损信息区域的信息内容，然后填充受损区域以实现图像恢复。为了解决实际应用中的图像遮挡问题，提出了一种挤压激励网络-深度卷积生成对抗网络（SE-DCGAN）。首先，使用SE-DCGAN可以生成许多新的清晰图像。然后，在生成的图像中，基于原始图像的上下文语义和未填充部分的编码来找到最相似的图像以填充原始图像。SE-DCGAN引入了具有强大拟合功能的maxout激活功能，以提高图像生成效率并避免图像生成冗余。基于CelebA，街景门牌号码和动漫头像的三个数据集的实验表明，我们的方法成功地预测了许多缺失区域。该方法提高了遮挡图像的识别率，产生了高质量的感知结果，并且足够灵活以处理各种遮罩或障碍物。