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An image super-resolution reconstruction method based on joint constraint

A super-resolution reconstruction and high-resolution image technology, applied in the field of image processing, can solve the problems of poor noise suppression ability and poor image reconstruction effect, achieve good reconstruction effect and improve suppression ability

Pending Publication Date: 2019-06-04
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0004] However, none of the above methods have a good ability to suppress noise, which makes the image reconstruction effect poor.

Method used

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  • An image super-resolution reconstruction method based on joint constraint
  • An image super-resolution reconstruction method based on joint constraint
  • An image super-resolution reconstruction method based on joint constraint

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Embodiment Construction

[0037] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0038] Such as figure 1 As shown, an image super-resolution reconstruction method based on joint constraints includes the following steps:

[0039] Step 1: Under the conditions based on low-order constraints, use the K-SVD algorithm for dictionary training and learning, and update the atoms in the dictionary one by one during the dictionary training process;

[0040] (1) Dictionary learning constrained optimization problem:

[0041]

[0042] Among them, D is a dictionary, D opt is the best dictionary after optimization, Λ is the sparse representation coefficient, ε is used to ensure sparsity, Z is the high-resolution image used for dictionary training, t s is the ...

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Abstract

The invention discloses an image super-resolution reconstruction method based on joint constraint, and the method comprises the steps: firstly, extracting an image block from a natural image, and under the condition of low-order constraint, carrying out dictionary training learning through a K-SVD algorithm, and updating atoms in the dictionary one by one in the dictionary training process; Secondly, obtaining a graphic block set with the same scale and multi-scale similarity with the image blocks through searching, performing weighted estimation on real codes by utilizing sparse codes of similar image blocks, and introducing difference values between the real codes and the obtained sparse codes as constraint items into an objective function; And finally, for an image block needing to be reconstructed, performing multiplication estimation by utilizing atoms and sparse coefficients in the dictionary to obtain a high-resolution image block. According to the method, the noise influence isweakened by introducing the constraint term, the reconstruction result is enhanced, and more image details are obtained.

Description

technical field [0001] The invention relates to an image super-resolution reconstruction method based on joint constraints, which belongs to the technical field of image processing. Background technique [0002] In recent years, image super-resolution reconstruction has attracted the attention of researchers, and has been widely used in various practical fields, such as medical images, video transmission, etc. However, there are still many problems to be solved in the actual application of image super-resolution reconstruction, such as: the influence of noise, the edge effect of the image, the ringing effect, etc., and the removal of the influence of noise is an urgent problem that needs to be alleviated. [0003] The reconstruction algorithms of super-resolution images mainly include methods based on interpolation, methods based on reconstruction and methods based on learning. Learning-based methods include: neighborhood embedding, sparse representation, and deep learning....

Claims

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Application Information

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IPC IPC(8): G06T3/40
CPCY02T10/40
Inventor 杨欣张一帆朱晨周大可李晓川
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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