Mr. Jiuzhe Wei Profile

Jiuzhe Wei

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 | 3 June 2024 Paper

Remote sensing image denoising algorithm based on improved Transformer network

Zhi Wang, Jiuzhe Wei, Yun Wang, Qiang Li

Proceedings Volume 13182, 131820Q (2024) https://doi.org/10.1117/12.3030732

KEYWORDS: Remote sensing, Denoising, Transformers, Image processing, Satellite imaging, Tunable filters, Satellites, Image denoising, Feature extraction, Quantum reading

Read Abstract +

In the current optical remote sensing field, it has continuously evolved into a multi-layered and multi-perspective observation system. Faced with the complexities of observation tasks, diverse observation methods, and the refinement of observation targets, there is a need for more in-depth research on denoising of remote sensing images. Traditional denoising algorithms often produce denoised images with overly smooth edge textures, leading to the loss of small targets within the images. Therefore, addressing the aforementioned issues, this paper proposes an improved denoising algorithm based on the Transformer network structure. This algorithm employs attention operations across channel dimensions and utilizes feature recalibration. This allows the model to determine the importance of various feature channels, thereby avoiding the significant computational overhead brought about by the traditional Transformer's self-attention enhancement in spatial dimensions. Moreover, the algorithm utilizes a U-shaped denoising module, which effectively reduces the semantic gap between image feature mappings, resulting in the restoration of better image features. The experiments indicate that when tested on remote sensing image datasets, the proposed algorithm outperforms current representative algorithms in both subjective and objective evaluation metrics. While effectively removing image noise, it also better preserves edge details and texture features, achieving superior visual results.

Proceedings Article | 18 December 2023 Paper

Noise generation model of low-light remote sensing camera based on physical mechanism

Zhi Wang, Qiang Li, JiuZhe Wei, Yun Wang

Proceedings Volume 12963, 1296317 (2023) https://doi.org/10.1117/12.3007720

KEYWORDS: Remote sensing, Cameras, Interference (communication), Data modeling, Denoising, Dark current, Signal to noise ratio, Process modeling, Convolution, Image processing

Read Abstract +

With the continuous development of the field of remote sensing, the field of space-based remote sensing is developing in the direction of all-sky time and intelligence. Since low-light remote sensing is used to detect ground objects under low illumination conditions such as night and dawn and dusk, it leads to the characteristics of low contrast, low brightness and low signal-to-noise ratio of remote sensing images. The low signal-to-noise ratio leads to a large number of complex physical noises which will drown the image features and seriously affect the recognition and interpretation of ground objects. In this paper, a real physical model construction method based on physical mechanism of low-light image is proposed, By solving the noise parameters of space-based low-light remote sensing camera, the specific noise distribution model is constructed, which is used to synthesize the training data of the training denoising network, Thus, it gets rid of the difficult and laborious calibration problem caused by the lack of space-based data. In addition, in order to simulate the real imaging scene of space-based remote sensing camera, a set of low-light remote sensing noise image data set based on real physical model is constructed for the first time, which effectively promotes the development of subsequent image denoising research.

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