Mr. Jun Lin Profile

Jun Lin

at Tianjin Univ

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 29 October 2018 Paper

Nighttime image haze removal and enhancement based on improved atmospheric scattering model

Jun Lin, Xingming Zhang, Huijuan Li, Zhihui Liu

Proceedings Volume 10836, 1083607 (2018) https://doi.org/10.1117/12.2502130

KEYWORDS: Atmospheric modeling, Image enhancement, Image restoration

Read Abstract +

In this work, we tackle the problem of nighttime image degradation caused by haze and weak illumination. We propose an improved atmospheric scattering model which can achieve single image haze removal and enhancement simultaneously. The input image firstly is decomposed into the structure layer and the texture layer based on the image total variation model. The structure layer contains the main scenes of original image including the haze and brightness, and the texture layer contains the detail and noise. In order to avoid the influence of the glow and multiple light sources on the estimation of atmospheric map, the glow layer then is stripped from the structure layer and the background layer can be calculated. Followed by performing the estimation method of atmospheric map and transmission we proposed, the structure layer can be restored according to the atmospheric scattering model. We finally fuse the restored background layer and optimized texture to obtain the haze-free and enhanced image. Experimental results demonstrate the efficacy of our proposed model.

Proceedings Article | 10 April 2018 Paper

Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

Chunming Tang, Jun Lin, Chunkai Chen, Yancheng Dong

Proceedings Volume 10615, 1061510 (2018) https://doi.org/10.1117/12.2302477

KEYWORDS: Fiber optic gyroscopes, Scattering, Video, RGB color model, Edge detection

Read Abstract +

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years