Ms. Jinfeng Lv Profile

Jinfeng Lv

at Shenyang Institute of Automation

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

SPIE Journal Paper | 16 June 2016

Kernel estimation for robust motion deblurring of noisy and blurry images

Shijie Sun, Huaici Zhao, Bo Li, Mingguo Hao, Jinfeng Lv

JEI, Vol. 25, Issue 03, 033019, (June 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.3.033019

KEYWORDS: Denoising, Motion estimation, Image analysis, Image processing, Deconvolution, Sun, Image quality, Cameras, Image denoising, Image filtering

Read Abstract +

Most state-of-the-art single image blind deblurring techniques are still sensitive to image noise, leading to serious performance degradation in their blur kernel estimation when the input image noise increases. We found that reliable kernel estimation could not be given by directly using denoising and existing deblurring algorithms in many cases. We focus on how to estimate a good blur kernel from a noisy blurred image via using the image structure. First, we applied denoising as a preprocess to remove the input image noise and then computed salient image structure of the denoised result based on the total variation model. We also applied a gradient selection method to remove those salient edges that have a possible adverse effect on blur kernel estimation. Next, we adopted a two-phase estimation strategy to obtain higher quality blur kernel estimation by jointly applying kernel estimation from salient image structure and iterative support detection (ISD) kernel refinement. Finally, we used the nonblind deconvolution method based on sparse prior knowledge to restore the latent image. Extensive experiments testify to the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.

Proceedings Article | 24 November 2014 Paper

Compression imaging based on Fourier transform optical system

Jinfeng Lv, Huaici Zhao, Chunyang Zhao

Proceedings Volume 9301, 93012S (2014) https://doi.org/10.1117/12.2073026

KEYWORDS: Fourier transforms, Imaging systems, Image processing, Signal processing, Compressed sensing, Cameras, Sensors, Image quality, Distortion, Data processing

Read Abstract +

Shannon / Nyquist sampling theorem indicates that during the sampling process the minimum sample rate must be more than the double of the band of the signal so that we can achieve images without distortion. High-frequency sampling leads to mass data and results in high cost of storage and transmission procedure. Compressed sensing indicates that we can sample data at far below the Nyquist frequency when the signals are sparse or can be represented as sparse on some orthogonal basis, and the signals can be recovered without distortion after some certain recovery algorithms. By this means the cost of storage and transmission can be reduced significantly. Unlike conventional optical imaging process, this paper presents a new imaging method using a Fourier transform lens system, which enables single-exposure and single-aperture compressed imaging. First, the Fourier transformation of image signals is accomplished after they pass through a Fourier transform optical system. Second, sparse sample data can be obtained after the spectrum signals pass the sensor array. The process mentioned above can be interpreted as that using a Fourier matrix and a sparse matrix to complete the measurement of the image signals. Third, we make use of fast iterative threshold recovery algorithm to compute the sampling values and obtain the target image signals. Compared with the conventional imaging methods, in the case of ensuring the image quality, our method can significantly reduce the number of samples, thus greatly reduce the data redundancy. Simulation results indicate that the imaging method proposed can be prospective.

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