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Shared k-SVD dictionary-based DVS visual video denoising method

A dictionary and video technology, applied in the field of image processing, can solve problems such as fast denoising, time-consuming dictionary iterative update, etc., and achieve the effect of clear object outline, fast speed and fast denoising speed

Active Publication Date: 2018-01-19
XIDIAN UNIV +1
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  • Claims
  • Application Information

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Problems solved by technology

Although this method does not require a large number of training samples and can better preserve the object features in the original image, it is difficult to meet the requirements of fast denoising due to the time-consuming iterative update process of the dictionary.

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  • Shared k-SVD dictionary-based DVS visual video denoising method

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Embodiment Construction

[0030] The present invention will be described in detail below with reference to the drawings and examples.

[0031] Reference figure 1 , The implementation steps of the present invention are as follows:

[0032] Step 1. Obtain the event stream of the dynamic vision sensor DVS.

[0033] 1a) Build a platform for dynamic vision sensor DVS:

[0034] 1a1) Connect the dynamic vision sensor DVS to the computer, open the FrontPanelUSB-DriverOnly-4.5.5.exe file in the FPGABoard Driver folder, and follow the prompts to install the dynamic vision sensor driver;

[0035] 1a2) Open the dynamic vision sensor test program GUI.exe in the GUI-Release folder, if the shooting scene can be displayed in the pop-up window, it means that it can work normally;

[0036] 1a3) Install commercial Microsoft Visual Studio 2013 software on the computer and configure Opencv3.0;

[0037] 1b) Use Microsoft Visual Studio 2013 to open DVS_record.sln in the DVS_record folder, and modify the event storage path DVS_EVENT_STO...

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Abstract

The invention discloses a shared k-SVD dictionary-based DVS visual video denoising method. The problem that an image object generated at a high frame rate is unclear in contour and long in consumed time is mainly solved. The scheme of the method is as follows: 1, installing the drive of a dynamic video sensor, capturing an event stream and storing the event stream; 2, converting the event stream into DVS images with clear contours, and grouping the images; 3, obtaining an optimized dictionary of a first frame of image in each group according to the k-SVD algorithm, and subjecting all the restimages to denoising treatment by using a learning dictionary obtained by the first frame of image in each group; 4, setting a video frame rate and a frame number, and carrying out transcoding treatment on the DVS images after being subjected to denoising treatment. According to the invention, an object has obvious profile and other characteristics under the condition that a high frame rate is ensured. Moreover, a good denoising effect and a high denoising speed can be achieved while the structure information of the object is reserved. The method can be used for the image pretreatment of DVS development.

Description

Technical field [0001] The invention belongs to the technical field of image processing, mainly relates to the denoising of DVS visualized video, and can be used for image preprocessing of DVS development. Background technique [0002] At present, traditional frame-based cameras have certain limitations for capturing moving objects. The dynamic vision sensor DVS is an event-based camera, which only pays attention to the changed pixels, and has the characteristics of no frame, high speed, and low bandwidth. These characteristics make DVS have good prospects in practical applications. [0003] DVS stores captured scenes in the form of events, so events can be used to visualize the scenes recorded by DVS, that is, to convert the event stream into frames of DVS visualization images, and then obtain DVS visualization videos. Information loss and noise interference are two problems in the visualization process. At present, a visualization method has been proposed, which is to visualiz...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T5/50
Inventor 谢雪梅李旺杜江石光明刘碗杨建秀
Owner XIDIAN UNIV
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