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Blind Restoration of Motion Blurred Images Using Improved Generative Adversarial Networks

A motion blurred image, blurred image technology, applied in the field of image processing, can solve the problems of unstable training, difficult calculation, complex parameter adjustment, etc., achieve the effect of academic value, reduce training time, and simplify the recovery process

Active Publication Date: 2022-02-15
CHONGQING UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

The problem with this type of method is that it is difficult to calculate and the process of tuning parameters is complicated. At the same time, it is still necessary to estimate the blur kernel in most cases.
Recently, some scholars have proposed a full network that does not estimate the fuzzy kernel, but the research is still in its infancy, the designed network is more complex, and the training is unstable

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  • Blind Restoration of Motion Blurred Images Using Improved Generative Adversarial Networks
  • Blind Restoration of Motion Blurred Images Using Improved Generative Adversarial Networks
  • Blind Restoration of Motion Blurred Images Using Improved Generative Adversarial Networks

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Embodiment Construction

[0034] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, taking the entire process of blind restoration of motion blurred images as an example. The preferred embodiments are only for illustrating the present invention, but not for limiting the protection scope of the present invention.

[0035] like image 3 As shown, the improved generative adversarial network includes a generative network G and a discriminative network D. G realizes the spatial mapping from the blurred image to the residual, where the residual represents the difference between the restored image and the blurred image, and D distinguishes the restored image from the clear image.

[0036] The specific process is: divide the blurred image into blocks, input them into G in batches, add the residual blocks output by 15 convolutional layers to the input blurred image blocks, and output the restored image blocks, and input them into D ...

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Abstract

The invention proposes a method for blind restoration of motion blurred images using an improved generative confrontation network, and obtains a clear image through the confrontation training of the generative network and the discriminant network. The generative network is a fully convolutional neural network combined with a residual network, which can deepen the network layer while reducing training time. The discriminant network is a binary classification network composed of convolutional layer, pooling layer and fully connected layer, which is used to determine the image restored by the generating network or the original clear image. The loss function adopts the minimum mean square error of smooth and unsaturated gradients, which can optimize network training and avoid gradient disappearance. At the same time, an image fidelity item is added to the loss function of the generation network to constrain the distribution of the restored image to be closer to the clear image.

Description

technical field [0001] The invention belongs to the technical field of image processing. Background technique [0002] Blind restoration of blurred images is a basic subject in image processing tasks. Restoring motion blurred images when the blur kernel is unknown is a serious ill-posed problem. In the traditional blind restoration of blurred images, the image and blur kernel are mainly constrained by various prior knowledge, and a strongly constrained image restoration model with generalization ability is established. Accurate blur kernel estimation is the key, and a fast model is required For this reason, these problems have become the bottleneck that affects the practical application of blurred image blind restoration technology for a long time. [0003] Deep learning has the characteristics of automatic feature extraction and powerful computing power, and has become the development direction of blurred image restoration research. Although deep learning can realize the ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06N3/04
CPCG06T5/003G06N3/045
Inventor 李伟红吴梦婷龚卫国
Owner CHONGQING UNIV
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