Image blind motion deblurring method based on CNN-Transform hybrid auto-encoder

An autoencoder, deblurring technology, applied in the fields of computer vision and image processing, which can solve the problems of poor visual effect and poor balance performance of advanced context information.

Active Publication Date: 2021-10-29
HUNAN UNIV +1
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

However, most methods do not perform well enough in the balance between spatial texture details and high-level contextual information, and the visual effect is not good enough.

Method used

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  • Image blind motion deblurring method based on CNN-Transform hybrid auto-encoder
  • Image blind motion deblurring method based on CNN-Transform hybrid auto-encoder
  • Image blind motion deblurring method based on CNN-Transform hybrid auto-encoder

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Embodiment Construction

[0035] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0036] refer to figure 1 , the method of the present embodiment includes two stages, respectively, a model training stage and a prediction stage, and the model training stage includes the following steps:

[0037] Step 1: prepare image deblurring standard data sets; the three motion blur data sets selected in this embodiment are: GoPro data set, DVD data set and NFS data set.

[0038] Step 2: Experimental data preprocessing; before entering the model training, the experimental data is randomly cut into a size of 256x256.

[0039] Step 3: Input the blurred pictures in the training set of the image deblurring standard dataset into the hybrid autoencoder part for restoration; the hybrid autoencoder part mainly includes two parts: CNN-Transformer hybrid encoder and decoder. The experimental data first enters the CNN-Transformer hybrid encoder fo...

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Abstract

An image blind motion deblurring method based on a CNN-Transform hybrid auto-encoder comprises two stages, namely a model training stage and a prediction stage, and the model training stage comprises the following steps: 1, preparing an image deblurring standard data set; 2, preprocessing experimental data; 3, inputting a blurred picture in a training set of the image deblurring standard data set into a hybrid auto-encoder part for recovery; 4, inputting the potential clear image generated by the hybrid auto-encoder and the corresponding target clear image in the standard data set into a discriminator, and returning the gradient after calculating the loss by the discriminator; and 5, receiving the gradient from the discriminator by the hybrid auto-encoder to perform parameter updating, wherein the model prediction stage comprises a step of inputting a blurred picture into a trained hybrid auto-encoder, and outputting the blurred picture as a deblurred clear picture. By using the method, a better image deblurring effect can be obtained, and image details can be recovered more clearly.

Description

technical field [0001] The invention belongs to the technical field of computer vision and image processing, and relates to an image blind motion deblurring method based on a CNN-Transformer hybrid autoencoder. Background technique [0002] As an important medium for transmitting information, images play an indispensable role in people's life and work. But the problem of image blurring is ubiquitous in life: due to problems such as camera shake, fast movement of objects or out of focus, the image quality will be reduced and the image will become blurred. Furthermore, various reasons: such as depth variation, occlusion of motion boundaries complicate blurring. Many shooting scenes are constantly changing over time and are not repeatable. Camera shake or blur caused by fast moving objects will most likely result in unusable images. How to effectively use these blurred images, the research on image deblurring methods is of great significance. [0003] Image deblurring has be...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/11G06T9/00G06N3/04G06N3/08
CPCG06T7/11G06T9/002G06N3/08G06T2207/20081G06T2207/20084G06T2207/20201G06N3/045G06T5/73
Inventor 陈华赵露露孙纪康张小刚王炼红潘政李磊谢冰心
Owner HUNAN UNIV
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