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Sparse representation-based single lens calculation imaging PSF estimation method

A computational imaging and sparse representation technology, applied in computing, image enhancement, image data processing, etc., it can solve the problems of long time-consuming iterative estimation process, inconvenient practical operation, and large difference in final ideal values.

Active Publication Date: 2015-11-11
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0008] In view of the large difference between the initial value of the blur kernel and the final ideal value in the existing single-lens imaging method, the iterative estimation process takes too long, and it is not convenient for practical operation, etc., the present invention proposes a single-lens computational imaging PSF estimation based on sparse representation method

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  • Sparse representation-based single lens calculation imaging PSF estimation method

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Embodiment Construction

[0051] Below, the present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0052] Such as Figure 5 As shown, a method for estimating PSF of single-lens computational imaging based on sparse representation provided in this embodiment includes the following steps:

[0053] Step 1: Use a single-lens camera to obtain a blurred image. The single-lens camera made in this experiment and the obtained blurred image are as follows: Figure 6 shown;

[0054] Step 2: Transform the image deblurring problem into a joint optimization problem. The clear image in the objective function is represented by the product of the overcomplete dictionary D and the sparse coefficient α, and the sparsity of the sparse coefficient is constrained. The final objective function can be expressed as:

[0055] m i n k , α ...

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Abstract

The invention discloses a sparse representation-based single lens calculation imaging PSF estimation method. In view of requirements on the PSF estimation speed and the precision in single lens calculation imaging, a sparse representation method is tried to estimate the PSF. Firstly, a clear image in a target function is represented to be a product of an over-complete dictionary and a sparse coefficient, and the sparse coefficient is constrained; then, a fuzzy kernel, the over-complete dictionary and the sparse coefficient are sequentially and alternatively estimated through iterative optimization algorithm, and thus the fuzzy kernel needed for single lens calculation imaging can be obtained. In the case of training the over-complete dictionary, a fuzzy image obtained by the single lens imaging system only needs to be known, more extra information is not needed, and as multiple times of convolution operation can be avoided during the calculation process, influences on image edge information restoration are reduced. The method is simple to operate, and is of great significance in the fields of image processing and camera design.

Description

technical field [0001] The invention mainly relates to the field of digital image processing, in particular to a single-lens computational imaging PSF estimation method based on sparse representation. Background technique [0002] At present, SLR cameras are playing an increasingly important role in people's daily life due to their advantages such as high-definition imaging quality, rich lens selection, fast response speed, and excellent manual control ability. However, in order to compensate for the geometric distortion and aberration of the lens in the SLR lens and further improve the imaging quality, the design of the SLR lens is becoming more and more complex, even including dozens of independent optical devices. While improving the imaging quality, complex lenses will undoubtedly increase the volume and weight of the lens, which will also greatly increase the cost of the lens. The increase in the size and weight of the lens has brought inconvenience to the daily use of...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 熊志辉李卫丽刘煜王炜徐玮
Owner NAT UNIV OF DEFENSE TECH
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