人工智能-基于多尺度神经网络的小区域图像修复.pdf

years have made a breakthrough in the accuracy and speed, but the memory hardware

conditions and GAN unbalanced Network training, for high resolution image, repair

area will appear blurred, there are clear lines, hard to fix the high frequency detail.

Second, the study found that the image restoration method based on convolution

neural networks often generated boundary artifact, deformation structure and clear

texture, as well as the influence of the network model itself, also may be due to the

convolutional neural network to remote image information and image to fill holes

caused by long-term association between modeling. At the same time, image repair is

widely used in real life, such as deleting unnecessary pedestrians in the image and

obtaining the sense of reality of background restoration. This problem is challenging

VGG-16 to extract the multi-scale features of the input image, and USES four groups

of cavity convolution with different rates to extract the image features of the

multi-scale receptive field, and then reconstructs the input image according to the

extracted features. Based on the Network of SRGAN (Super-Resolution Adversarial

Network), the texture detail restoration Network added void convolution, and restored

the texture details by using skip connection and multi-scale feature fusion. This

method can effectively solve the problems of structure distortion and texture blur, and

improve the quality of image restoration.

2) Pedestrian removal for image restoration in small areas: Aiming at the

problem of filtering out redundant pedestrians occupying small background areas that

layer ignores the correlation between generated image blocks, which may lead to the

defects of malleability and continuity of the final result. In this paper, we improve the

unified feed forward generation network based on the novel image context attention

layer. The correlation between the generated image blocks is enhanced by changing

the transmission mechanism of the context attention layer. At the output end of the

contextual attention layer, the feature aggregation of multi-scale receptive fields is

carried out through four groups of cavity convolution with different rates, which

ensures the consistency between the structure of the final reconstructed features and

the environment. The improved image repair network model based on contextual

attention can enhance the ability to recognize the image structure of the surrounding