Mr. Ruilin Xie Profile

Ruilin Xie

SPIE Involvement:

Author

Publications (1)

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 | 13 June 2024 Paper

Generalized image denoising based on MLP denoiser and diffusion model

Jing Wu, Ruilin Xie, Hao Wu, Guowu Yuan

Proceedings Volume 13180, 131800E (2024) https://doi.org/10.1117/12.3033714

KEYWORDS: Denoising, Education and training, Diffusion, Image denoising, Image restoration, Signal to noise ratio, Speckle, Overfitting, Feature extraction

Read Abstract +

Comprehending the intricacies of image structure and noise distribution is pivotal in employing paired training methods. Nevertheless, these approaches encounter difficulties when attempting to generalize to images characterized by unknown noise distributions. In response to this challenge, we propose a two-stage image denoising method designed to enhance generalization performance. In the initial stage, we introduce a preliminary denoiser based on a Multilayer Perceptron (MLP). This denoiser utilizes an implicit structural prior to forecast preliminary denoising results, ensuring alignment with the clean image structure. This stage necessitates training with a limited set of paired low-noise images. In the subsequent stage, we incorporate initial denoising results as guiding conditions for denoising diffusion null-space model (DDNM), we proficiently harness the generated diffusion prior, markedly amplifying the denoising model's generalization capability. We utilize a pretrained unconditional diffusion model, obviating the need for supplementary training or network optimization, thereby resulting in an economical overall training cost for the entire method. Through extensive experimental validation encompassing diverse datasets, noise types, and intensity levels, our method consistently outperforms alternative image denoising techniques in both denoising performance and generalization ability.

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