Method and device for removing motion blur of image

A motion blurring and deblurring technology, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as single estimation, ringing effect, and spatially non-uniform motion blur

Active Publication Date: 2019-11-22
ZHEJIANG UNIV
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Spatially non-uniform motion blur caused by real camera shake is more difficult to remove than classical spatially uniform motion blur
Because diffe

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
  • Method and device for removing motion blur of image
  • Method and device for removing motion blur of image
  • Method and device for removing motion blur of image

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] Embodiment 1, a device for removing motion blur from an image, the structural block diagram is as follows figure 2 As described above, it includes an input image processing module 10 , a reference image block search module 20 , a blur kernel estimation module 30 , a blur kernel selection module 40 , a non-blind deconvolution module 50 , and an image reconstruction module 60 .

[0050] The input image processing module 10 is used to process the original motion blurred image and obtain a series of overlapping image blocks {P i}; Specifically, the input image processing module 10 includes an image processing unit 12 and an image block division unit 14; the image processing unit 12 is used to read the image to be processed, identify the image format and decompress it, calculate the image space distance, color, The processing of texture and fuzzy features to obtain blurred images and features; and the resulting blurred images and features are input to the image block divisi...

Embodiment 2

[0056] Embodiment 2, a method for image de-blurring using the device described in Embodiment 1 (based on L 0 image deblurring methods with sparsity blur kernel quality metric), such as figure 1 As shown, the image de-blurring method mainly includes the following steps (S1-S6),

[0057] Step S1, using features such as spatial distance, color, texture, and blur, divide the original blurred image into a series of overlapping image blocks {P i}={P 1 , P 2 ,...,P N}, where N is the number of image blocks. The overlapping image blocks {P i}={P 1 , P 2 ,...,P N} can be formulated according to the spatial position, color, texture, and fuzzy features of the image block; specifically, it can be: make the similarity of the spatial distance, color, texture, and fuzzy features of pixels in the same image block high, and not in the same image block The similarity of the spatial distance, color, texture, and fuzzy features of pixels in the same image block is low, wherein the measur...

example 1

[0071] Example 1, this example performs image motion blur removal according to the method described in Example 2, and the steps are as follows:

[0072] Input: blurred image y.

[0073] Output: final deblurred image

[0074] Parameter settings: overlapping image block size W×W, domain range ±N, step size S; where W=64 pixels, N=64 images

[0075] pixel, S=32 pixels.

[0076] Step S1:

[0077] Divide the blurred image y into n overlapping image blocks of size W×W {y l}.

[0078] Step S2:

[0079] For all l=1, 2, .., n, let for y l A set of reference image blocks; where, for y l All W×W, 2W×2W, 4W×4W size image block collections within the ±N neighborhood of .

[0080] Step S3:

[0081] For all l=1, 2, ..., n, and the y obtained in step S2 l A set of reference image blocks of For all reference image blocks in , use the existing blur kernel estimation method based on maximum a posteriori probability to get the preliminary estimated blur kernel

[0082] Step S...

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 method and a device for removing motion blur of an image. The method comprises: firstly, performing overlapped image block division on an input blurred image; selecting a group of reference image blocks from the blurred image for each overlapped image block; obtaining a series of preliminarily estimated blurred kernels for all reference image blocks corresponding to eachoverlapped image block by using an existing blurred kernel estimation method; obtaining a plurality of overlapped blurred image blocks, selecting a local optimal blurred kernel from the overlapped blurred image blocks by using an L0 sparsity blurred kernel quality measurement standard, finally performing non-blind deconvolution on the corresponding overlapped blurred image blocks by using each local optimal blurred kernel to obtain deblurred image blocks, and splicing the deblurred image blocks together to obtain a final deblurred image. When the motion blur of the image is removed, the continuously changing non-uniform motion blur can be effectively removed, and the ringing effect is reduced as much as possible.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image motion blur removal method and device. Background technique [0002] Image motion blur is caused by the relative motion of the camera and the scene being photographed during the exposure. With the popularity of smart phones, it has gradually become the most popular camera device today. Limited by the size limitation of the device itself and the design cost considerations, the aperture size of the mobile phone camera is very limited, and the area of ​​the photosensitive element per pixel of the image sensor is also very limited, which makes the light-sensing ability of the mobile phone camera relatively weak. To get enough photons per pixel, the camera needs to capture light for tens to hundreds of milliseconds. Therefore, when taking pictures with a mobile phone in hand, the problem of motion blur becomes particularly prominent. Image deblurring is to...

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
IPC IPC(8): G06T5/00
CPCG06T5/003G06T2207/20201G06T2207/20021G06T2207/20221
Inventor 王维东陈佳云
Owner ZHEJIANG UNIV
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