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Image super resolution rebuilding method based on sparse representation and various residual

A technology of super-resolution reconstruction and sparse representation, which is applied in the field of image processing and can solve problems such as the increase of resolution magnification.

Inactive Publication Date: 2013-05-22
HANGZHOU DIANZI UNIV
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Problems solved by technology

Super-resolution reconstruction methods based on multiple low-resolution images usually require sub-pixel registration of low-resolution images to obtain position changes between images. The increase in magnification is very limited

Method used

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Embodiment Construction

[0028] The present invention will be described in detail below in conjunction with implementation examples.

[0029] For a color RGB image, first convert it to a YUV image, perform super-resolution reconstruction on the Y component, and the UV component is enlarged by interpolation, and then convert the YUV image into an RGB image; for a grayscale image, directly on the grayscale image Perform super-resolution reconstruction.

[0030]Performs a 3x upscaling on the input low-resolution image. Divide the low-resolution image into several image sub-blocks with a size of 3×3, and the corresponding high-resolution image sub-block size is 9×9. In order to maintain the compatibility between the image sub-blocks, the low-resolution image sub-blocks If a block takes 1 overlapping pixel, the corresponding high-resolution image sub-block overlaps by 3 pixels.

[0031] Step (1) Calculate the residual, and obtain the high-frequency part and low-frequency part of the residual, specificall...

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Abstract

The invention relates to an image super resolution rebuilding method based on sparse representation and various residual. The image super resolution rebuilding method based on sparse representation and various residual includes the steps: first, calculating residual between images formed after the existing high resolution image and a low resolution image are amplified through an interpolation and calculating a high frequency portion and a low frequency portion of the residual; second, building a sample pair through low resolution image sample characteristic and corresponding the high frequency portion and low frequency portion of the image residual, utilizing a texture meta structure to classify the samples by regarding the low resolution sample as a standard and utilizing a singular value decomposition (KSVD) method to train each type of a sample to obtain a dictionary pair of the low resolution sample, the high frequency portion and low frequency portion of the image residual; finally, choosing the dictionary pair and combing the final image residual with the interpolation result of the low resolution image to obtain a high resolution image according to the texture meta structure type of the test samples. The image super resolution rebuilding method based on sparse representation and various residual just needs to rebuild the image residual, combines the interpolation image to rebuild the high resolution image and improves a rebuilding result of the high resolution image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for performing super-resolution reconstruction on an image, in particular to an image super-resolution reconstruction method based on sparse representation and multiple residuals. Background technique [0002] Image super-resolution reconstruction has a wide range of applications in various fields such as medical imaging and satellite imaging. It refers to the method of reconstructing a high-resolution image from one or more input low-resolution images. Compared with the method of acquiring high-resolution images with hardware, it has a lower cost. [0003] Image super-resolution reconstruction can generally be divided into three types of super-resolution reconstruction methods based on interpolation, based on multiple low-resolution images, and based on learning. The images reconstructed by interpolation-based super-resolution methods tend to be over-smoothed, s...

Claims

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Application Information

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IPC IPC(8): G06T5/50
Inventor 陈华华姜宝林姜芳芳刘超
Owner HANGZHOU DIANZI UNIV
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