Multi-level image restoration method based on partial-to-overall attention mechanism

A repair method and attention technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of ignoring the internal semantic continuity of the region, unreasonable repair results, etc.

Pending Publication Date: 2020-05-08
FUDAN UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

However, this method pays too much attention to the utilization of existing information and ignores th

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  • Multi-level image restoration method based on partial-to-overall attention mechanism
  • Multi-level image restoration method based on partial-to-overall attention mechanism
  • Multi-level image restoration method based on partial-to-overall attention mechanism

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Embodiment Construction

[0048] The embodiments of the present invention will be described in detail below, but the protection scope of the present invention is not limited to the examples.

[0049] use figure 1 The network structure shown, with the dataset Paris StreetView [5] (Paris street view data set, including a total of 14,900 training pictures and 100 test pictures) to train the network to obtain an image restoration model.

[0050] The specific steps are:

[0051] (1) Before training, initialize the network parameters randomly, and uniformly adjust the image size in the training set to 610×350;

[0052] (2) During training, the image size is randomly cropped to 256×256, and normalized to the [0,1] interval. The Adam optimizer is used to update the model parameters, the initial learning rate of the generator is 0.0001, and the learning rate of the discriminator is one-tenth of the generator. Minimize the loss function using mini-batch stochastic gradient descent. The batch size is set to ...

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Abstract

The invention belongs to the technical field of digital image intelligent processing, and particularly relates to a multi-level image restoration method based on a partial-to-overall attention mechanism. Image restoration refers to replacing and generating lost or defective image data by using an algorithm. The method comprises the steps that a multi-level deep convolutional neural network generator structure is provided; and an attention mechanism convolution layer from is partially and wholly integrated into a generator and a discriminator of the network. An image block discriminator and four loss functions of reconstruction loss, perception loss, style loss and adversarial loss are introduced in the training process of the network to assist a generator to learn an image restoration task. Experimental results show that the restored image with vivid details and a reasonable overall structure can be generated, and the image restoration problem is effectively solved.

Description

technical field [0001] The invention belongs to the technical field of digital image intelligent processing, and relates to an image restoration method, more specifically, to a multi-level image restoration method based on a part-to-whole attention mechanism. Background technique [0002] As the media people use to store information has changed, so has the definition of the task of image restoration, from the restoration of damaged frescoes during the Renaissance to the restoration of aging paper photographs in earlier years , and then to the current processing of digital pictures stored on the computer. It should be emphasized that the application of image repair is not limited to the function of "repair". In addition, the current repair technology can also be applied to object removal, watermark removal, occlusion removal, face acne removal, skin smoothing, etc. Scenes. [0003] Early image inpainting techniques are mainly divided into two categories based on the size of...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/005G06N3/08G06T2207/20081G06N3/045Y02T10/40
Inventor 颜波陈鹤丹
Owner FUDAN UNIV
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