Infrared image super-resolution reestablishing method based on compressed sensing theory

A super-resolution reconstruction, infrared image technology, applied in the direction of graphic image conversion, image data processing, instruments, etc., can solve the problems of lack of stability, high algorithm complexity, long calculation time and so on

Inactive Publication Date: 2015-07-08
CHONGQING UNIV
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Problems solved by technology

[0008] In view of the above-mentioned problems existing in the prior art, the purpose of the present invention is to solve the technical problems of high complexity of existi

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  • Infrared image super-resolution reestablishing method based on compressed sensing theory
  • Infrared image super-resolution reestablishing method based on compressed sensing theory
  • Infrared image super-resolution reestablishing method based on compressed sensing theory

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[0107] Example: see figure 1 -5. A super-resolution reconstruction method of infrared images based on compressed sensing theory. The image is collected to determine the size of the image block, that is, the value of the number of rows and columns m and n and the number of overlapping pixels k of the block, and then the image is analyzed. piece. Write out the downsampling matrix D and the sparse transformation matrix H according to the definition of the block size, so that the sensing matrix A is obtained by A=DH. The low-resolution image block Y is taken out and arranged into a column vector y, and the problem of high-resolution reconstruction of the image block is transformed into the solution of y=Ax′. According to the theory of compressed sensing, when x' has sparse characteristics, the sparse coefficient x' can be reconstructed according to y and A, and finally a high-resolution image block is obtained according to x=Hx', and all high-resolution image blocks are spliced ​...

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Abstract

The invention relates to an infrared image super-resolution reestablishing method based on the compressed sensing theory. According to the method, low-resolution images are used as a foundation and are subjected to partitioning, low-resolution image blocks are regarded as downsampling observation corresponding to high-resolution image blocks, a downsampling model is established, and a downsampling matrix is written. A sparse transformation matrix of the high-resolution image blocks is established and is multiplied by an observation matrix, and a sensing matrix is obtained. According to the low-resolution blocks and the sensing matrix, an OMP algorithm is used for reestablishing sparse coefficients of the high-resolution image blocks, then the sparse transformation matrix is multiplied by the sparse coefficients, and the high-resolution image blocks are obtained. Finally, all the high-resolution image blocks are spliced, and a high-resolution reestablished image is obtained. The method has the advantages of being easy to achieve, quick in operation, stable in performance and good in anti-noise effect, difference operation is used for generating a difference transformation matrix and achieving sparse transformation, complex computing of redundant dictionary training is avoided, shot noise of images can be well removed, and the noise reduction advantage is achieved.

Description

technical field [0001] The invention relates to image processing technology, in particular to an infrared image super-resolution reconstruction method based on compressed sensing theory. Background technique [0002] In the field of image applications, insufficient detail resolution is an important factor that limits the visual effect of images and the performance of target understanding and recognition. In the field of medical imaging such as infrared imaging, image resolution is severely limited by the number and size of pixels in the detector array. The most direct way to improve image resolution is to improve the image sensor manufacturing process, that is, reduce the pixel size from the hardware and increase the number of pixels in the detector array. However, reducing the pixel size introduces image noise, and increasing the number of pixels reduces the sensor's operating efficiency and reliability. At the same time, the manufacturing cost of high-resolution imaging ...

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Application Information

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IPC IPC(8): G06T3/40
Inventor 毛玉星王艳严冬梅周晋涛李超
Owner CHONGQING UNIV
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