A Single-Lens Computational Imaging PSF Estimation Method Based on Sparse Representation

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

Active Publication Date: 2017-08-11
NAT UNIV OF DEFENSE TECH
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Single-Lens Computational Imaging PSF Estimation Method Based on Sparse Representation
  • A Single-Lens Computational Imaging PSF Estimation Method Based on Sparse Representation
  • A Single-Lens Computational Imaging PSF Estimation Method Based on Sparse Representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] Below, the present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0053] 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:

[0054] 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;

[0055] 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:

[0056]

[0057] Among them, b represents the blurred image, k represents the blur kernel, D represents the overcomplete dictionary, A represent...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a single-lens computational imaging PSF estimation method based on sparse representation. Aiming at the requirements of PSF estimation speed and accuracy in single-lens computational imaging, try to estimate PSF with sparse representation. Firstly, the clear image in the objective function is represented as the product of over-complete dictionary and sparse coefficients, and the sparse coefficients are constrained. Then the fuzzy kernel, over-complete dictionary and sparse coefficients are alternately estimated by iterative optimization algorithm. The blur kernel needed for single-lens computational imaging can be obtained. When the complete dictionary is trained, it only needs to know the blurred image obtained by the single-lens imaging system without more additional information, and avoids multiple convolution operations in the calculation process, reducing the impact on the edge information of the restored image . The method is simple to operate and has very important 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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap