Image super-resolution processing method based on probabilistic generative model

A technology for generating models and processing methods, applied in the field of image processing, which can solve the problems of limiting the resolution of high-resolution images, not taking into account the use of model prior information, and being unable to utilize all information, so as to achieve the effect of improving the resolution.

Active Publication Date: 2019-10-11
XIDIAN UNIV
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Problems solved by technology

However, there are still deficiencies: this method does not take into account the prior information of the model, nor does it consider the uncertain factors implied by the hidden variables in the model, so that the method cannot use all the information contained in the original image. Only part of the information is used to generate the corresponding high-resolution image
However, there are still shortcomings: this method only uses a shallow probability model, and only uses the information on the surface of the low-resolution image, and does not take into account the hidden information in the high-resolution / low-resolution image block pair, where The methods involved cannot generate more information to improve the resolution of the image, limiting the resolution of the final high-resolution image

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  • Image super-resolution processing method based on probabilistic generative model
  • Image super-resolution processing method based on probabilistic generative model
  • Image super-resolution processing method based on probabilistic generative model

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Embodiment Construction

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] refer to figure 1 , The specific implementation steps of the present invention are as follows.

[0036] Step 1. Input image.

[0037] Input multiple high-resolution optical images into the training set.

[0038] Multiple high-resolution optical images different from those in the training set are input into the test set.

[0039] Step 2. Get training samples.

[0040] For each high-resolution optical image in the training set, the bicubic interpolation method is used after down-sampling to obtain a low-resolution image with the same size as the high-resolution image.

[0041] The down-sampling process refers to down-sampling the high-resolution optical image with scaling factors of 2, 3, and 4 to obtain low-resolution images with sizes of 8×8 pixels, 10×10 pixels, and 12×12 pixels.

[0042] Image blocks of the same size are extracted from the same position o...

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Abstract

The invention discloses a super-resolution image processing method based on a probability generation model. The method comprises: (1), inputting an image; (2), obtaining a training sample; (3), obtaining a testing sample; (4), training a probability generation model; and (5), testing the probability generation model. According to the invention, compared with the common method, the super-resolutionimage processing method enables the generated high-resolution image to have higher resolution than that generated by the common method and to have more information; and with the prior information ofthe probability generation model and information implied by a hidden variable, the image super-resolution speed is increased. The method is a high-efficiency super-resolution image processing method.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image super-resolution processing method based on a probability generation model in the technical field of image super-resolution processing. The present invention can be used to first generate corresponding high-resolution image blocks by using low-resolution image blocks, then generate high-resolution images corresponding to the original low-resolution images, and then regenerate high-resolution images corresponding to the original low-resolution images. Background technique [0002] Super-resolution optical images can overcome the limitations of low-resolution optical images and have shown promising results in many applications such as medical diagnosis, remote sensing, computer vision, and surveillance. To obtain high-resolution optical images, the most direct way is to use high-resolution image sensors, but due to the limitations of the manufacturing proces...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40G06K9/62
Inventor 陈渤李婉萍张昊王正珏
Owner XIDIAN UNIV
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