Mr. Bo Zhang Profile

Bo Zhang

at Shanghai Institute of Satellite Engineering

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 | 23 November 2024 Paper

An algorithm for enhancing low-light remote-sensing images based on zero-reference deep curve estimation (Zero-DCE)

Hong Dai, Bo Zhang, Hui Zhao, Yinqiang Su, Ximing Zhang, Baopeng Li, Mingyang Yang, Xuewu Fan

Proceedings Volume 13239, 132390A (2024) https://doi.org/10.1117/12.3036367

KEYWORDS: Remote sensing, Image enhancement, Image quality, Image processing

Read Abstract +

Low-light remote sensing technology is crucial for surface observation during twilight and lunar phases; however, the acquired images often suffer from low contrast, low brightness, and low signal-to-noise ratios, which adversely affect observation quality. Traditional low-light image enhancement algorithms, such as Histogram Equalization, Gamma Correction, and Adaptive Histogram Equalization, can improve visual outcomes but also suffer from issues such as over-enhancement, loss of detail, noise amplification, and insufficient adaptability. To address these limitations, this paper proposes a low-light remote sensing image enhancement method based on Zero-Reference Deep Curve Estimation (Zero-DCE). This approach does not require paired samples and guides network learning through a non-reference loss function, making it particularly suitable for enhancing remote sensing images in low-light environments. Due to the lack of dedicated low-light remote sensing datasets, this study utilizes images from the UCMerced dataset to create simulated low-light remote sensing images for model fine-tuning. All color images are converted to grayscale to align with the characteristics of satellite-based low-light remote sensing images and to simplify the training process. Experimental results demonstrate that the proposed method significantly outperforms traditional techniques in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), while also excelling in denoising and preserving texture authenticity. The optimized Zero-DCE++ not only maintains the original performance but also significantly reduces computational costs and enhances inference speed, which is of great importance for real-time low-light remote sensing image processing on satellite platforms.

Proceedings Article | 23 November 2024 Paper

Super-resolution reconstruction for low-light imaging using intensified CMOS camera: SNR analysis and algorithm improvement

Hui Zhao, Bo Zhang, Hong Dai, Xuewu Fan, Chao Gao, Ge Zhang, Yinqiang Su, Zhe Bai, Baopeng Li, Mingyang Yang

Proceedings Volume 13239, 132391M (2024) https://doi.org/10.1117/12.3037336

KEYWORDS: Signal to noise ratio, Reconstruction algorithms, Super resolution, Image restoration, Modulation transfer functions, Cameras, Image enhancement, Microchannel plates, CMOS cameras, Image resolution

Read Abstract +

It is difficult for traditional CMOS camera to obtain clear images under extremely low-light conditions for example the new moon or the quarter moon because the photons generated are so few that the signal-to-noise ratio (SNR) is much lower than what is necessary to resolve finer details. Being different from traditional CMOS camera, intensified CMOS, named as ICMOS camera can greatly amplify the very limited arriving photons through external photoelectric effect and thus the corresponding SNR could be improved a lot for low-light conditions. In previous studies, by fusing a series of low-light images having sub-pixel displacement between each other through classical iterative back projection (IBP) reconstruction algorithm, not only the resolution is enhanced but also the SNR increases as well. However why the SNR can be improved through super-resolution reconstruction is not theoretically answered yet. Therefore in this manuscript two contributions are made. In the first place, the characteristics of sub-pixel super-resolution low-light imaging are firstly further investigated. By introducing the concept of spectral SNR, the analytical expression of the SNR before and after super-resolution reconstruction is established, based on which it is concluded that the MTF boosting generated by super-resolution reconstruction is one important factor that can bring in the SNR increment besides inherent noise reducing characteristic of the super-resolution reconstruction algorithm itself. In the second place, by combing the IBP based super-resolution reconstruction algorithm, the FFT (Fast Fourier Transform) based single image amplification and image enhancement methods together, better reconstruction results could be obtained.

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