The invention provides an image inpainting method and system based on antagonistic generation neural network, which comprises the following steps: firstly, constructing a self-encoder convolutional neural network (including encoder and encoding discriminator), a decoder (generator) convolutional neural network, a discriminator convolutional neural network, a global discriminator, and a local discriminator; Then, different loss functions are constructed for the five networks, and the whole network is trained by step-by-step training. Finally, when the network training is completed, the defect image is put into the network for repairing, and the result graph generated by the decoder (generator) is the final repairing result graph. The invention has the advantages that: the potential constraint of the image is kept and the image is thinned; an end-to-end image restoration network is implemented. The dependence of the repair network on the missing position mask information of the image iseliminated. The robustness in practical application is improved.

Image inpainting method and system based on antagonistic generation neural network

The invention discloses a motion blurred image blind restoration method using an improved generative adversarial network. A clear image is obtained through adversarial training of a generative networkand a judgment network, which is characterized in that a blurring kernel does not need to be estimated, and end-to-end image blind restoration can be realized. The generative network is a full convolutional neural network combined with a residual error network, can deepen a network layer, and shortens the training time. The judgment network is a binary classification network consisting of a convolutional layer, a pooling layer and a full connection layer, and is used for judging the image restored by the generative network or the original clear image. A loss function adopts a least mean square error of smooth and non-saturation gradients, can optimize network training, and avoids gradient disappearance. An image fidelity term is added in the loss function of the generative network and used for constraining distribution of the restored image to be closer to the clear image.

Motion blurred image blind restoration method using improved generative adversarial network

The invention discloses a composite degraded image high-quality reconstruction method based on a generative adversarial network. The method is mainly used for low-quality images with various quality reduction problems including haze, system noise, low illumination and compression distortion and the like. According to the method, a composite degraded image high-quality reconstruction method based on a generative adversarial network is established from the perspective of composite factor degraded image reconstruction, and reconstruction of a degraded image combined by factors such as haze, low illumination, compression, system noise and optical blurring can be completed; Secondly, an asymmetric generation network is adopted, so that the parameter quantity of the model is greatly reduced, andthe model is easy to train and use; Furthermore, the end-to-end idea is adopted, so that the architecture of the reconstruction system is simplified, and preprocessing and post-processing are omitted; And finally, the generation network is completely composed of convolution layers, and a composite degraded image with any size can be input for reconstruction.

A composite degraded image high-quality reconstruction method based on a generative adversarial network

The invention discloses a satellite image super-resolution method combining an adversarial network with an aerial image a priori. Firstly, an image pair composed of a 16-level noisy image and a corresponding 16-level non-noisy image is used for training a denoising model, and then the image super-resolution model is trained by using clear aerial data. Because there is no satellite image and aerialimage pairs, clear aerial images are used to construct an external priori dictionary of GMM model in the post-processing of the generated super-resolution images, and then the internal unclear satellite images are guided to be reconstructed. In order to further improve the image quality, Gaussian filter is used to sharpen the image after reconstruction. Finally, the high-resolution image of the original satellite image is obtained, and the visual quality of the image is improved based on the original satellite image. The effectiveness of the scheme can also be seen from the experiment. It provides an effective way to solve the problem of satellite image super-resolution and image quality improvement under the condition of conditional constraints in reality.

A satellite image super-resolution method based on adversarial network and aerial image a priori

The invention provides a real scene small object detection method based on a multi-task generation antagonism network, belonging to the technical field of object detection in the computer vision field. The detection method introduces a multi-task generation antagonistic network into object detection, and realizes the establishment of a small object detection network and the completion of the smallobject detection of an image by making the generator network and the discriminator network in the multi-task generation antagonistic network learn from each other in a game mode. The small object detection method overcomes the difficulty of low detection accuracy of the object detection method in the real scene at the present stage, and promotes the application of the object detection method based on depth learning in the real scene.

A small object detection method in real scene based on multi-task generation antagonistic network

The invention discloses a face image super-resolution reconstruction method based on a discriminable attribute constraint generative adversarial network, and belongs to the field of digital images/video signal processing. The method comprises the following steps: firstly, designing a processing flow of face detailed information enhancement; secondly, designing a network structure according to theflow, and acquiring an HR image from an LR image through the network; and lastly, performing face verification accuracy evaluation on the HR image through a face recognition network. Through adoptionof the method, enhancement including LR face image detailed information can be completed, and the accuracy of face verification is increased. Secondly, the generative network completes compensation ofimage high-frequency information firstly, then completes image amplification by subpixel convolution, and finally completes stepwise image amplification through a cascade structure, thereby completing enhancement of image detailed information. An attribute constraint module are trained together with a perception module and an adversarial model in order to perform fine adjustment of the performance of a network reconstructed image. Finally, a reconstructed image of the generative network is input into a face verification network, so that the accuracy of face verification is increased.

Face image super-resolution reconstruction method based on discriminable attribute constraint generative adversarial network

Many pieces of research have been conducted on image-restoration techniques to recover high-quality images from their low-quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image-restoration methods for captured images. A joint deep-network-based image-restoration algorithm is proposed to establish a restoration framework for image deblocking and super-resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super-resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end-to-end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors’ method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, in terms of both subjective and objective performances.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2018.6113

Deep-network based method for joint image deblocking and super-resolution

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Face image super-resolution reconstruction method based on discriminable attribute constraint generative adversarial network

Deep-network based method for joint image deblocking and super-resolution