KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping
Infrared imaging systems are widely used across industries. However, their output images often exhibit striped noise due to the nonuniform response of the detection system, which significantly affects image quality and visual fidelity. To address challenges such as incomplete stripe removal, potenti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10978030/ |
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| author | Lingxiao Li Xin Wang Dan Huang Yunan He Zhuqiang Zhong Qingling Xia |
| author_facet | Lingxiao Li Xin Wang Dan Huang Yunan He Zhuqiang Zhong Qingling Xia |
| author_sort | Lingxiao Li |
| collection | DOAJ |
| description | Infrared imaging systems are widely used across industries. However, their output images often exhibit striped noise due to the nonuniform response of the detection system, which significantly affects image quality and visual fidelity. To address challenges such as incomplete stripe removal, potential loss of image details and textures, and the generation of artificial artifacts during destriping, we propose a novel stripe removal method based on a knowledge-embedded diffusion model (KEDM). This approach effectively integrates the spatial distribution characteristics of stripe noise with an innovative, data-driven diffusion network model, creating a hybrid knowledge and data-driven framework for stripe correction. The core components of KEDM are the latent diffusion model (LDM) architecture and the directional wavelet convolution module (DWCM). Specifically, LDM leverages a pretrained variational autoencoder (VAE) to transform the input image into latent feature space for efficient diffusion propagation, reducing computational complexity while preserving image restoration quality. Meanwhile, DWCM uses wavelet convolution operations to construct prior loss functions for stripe noise, precisely guiding the diffusion reconstruction process to achieve a clean, stripe-free image. Empirical evaluations on several benchmark datasets demonstrate that the proposed KEDM outperforms other state-of-the-art destriping algorithms in terms of visual quality and quantitative metrics, validating its excellent performance. |
| format | Article |
| id | doaj-art-7cdb3f45045245a5b5241dc1863bffd4 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-7cdb3f45045245a5b5241dc1863bffd42025-08-20T03:48:02ZengIEEEIEEE Photonics Journal1943-06552025-01-011731910.1109/JPHOT.2025.356483110978030KEDM: Knowledge-Embedded Diffusion Model for Infrared Image DestripingLingxiao Li0https://orcid.org/0000-0001-8491-6814Xin Wang1Dan Huang2Yunan He3Zhuqiang Zhong4https://orcid.org/0000-0002-4293-8246Qingling Xia5School of Science, Chongqing University of Technology, Chongqing, ChinaSchool of Science, Chongqing University of Technology, Chongqing, ChinaChina Research and Development Academy of Machinery Equipment, Beijing, ChinaSchool of Science, Chongqing University of Technology, Chongqing, ChinaSchool of Science, Chongqing University of Technology, Chongqing, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing, ChinaInfrared imaging systems are widely used across industries. However, their output images often exhibit striped noise due to the nonuniform response of the detection system, which significantly affects image quality and visual fidelity. To address challenges such as incomplete stripe removal, potential loss of image details and textures, and the generation of artificial artifacts during destriping, we propose a novel stripe removal method based on a knowledge-embedded diffusion model (KEDM). This approach effectively integrates the spatial distribution characteristics of stripe noise with an innovative, data-driven diffusion network model, creating a hybrid knowledge and data-driven framework for stripe correction. The core components of KEDM are the latent diffusion model (LDM) architecture and the directional wavelet convolution module (DWCM). Specifically, LDM leverages a pretrained variational autoencoder (VAE) to transform the input image into latent feature space for efficient diffusion propagation, reducing computational complexity while preserving image restoration quality. Meanwhile, DWCM uses wavelet convolution operations to construct prior loss functions for stripe noise, precisely guiding the diffusion reconstruction process to achieve a clean, stripe-free image. Empirical evaluations on several benchmark datasets demonstrate that the proposed KEDM outperforms other state-of-the-art destriping algorithms in terms of visual quality and quantitative metrics, validating its excellent performance.https://ieeexplore.ieee.org/document/10978030/Conditional diffusion modelinfrared image destripingknowledge-embedded diffusion modelstripe priorwavelet transform |
| spellingShingle | Lingxiao Li Xin Wang Dan Huang Yunan He Zhuqiang Zhong Qingling Xia KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping IEEE Photonics Journal Conditional diffusion model infrared image destriping knowledge-embedded diffusion model stripe prior wavelet transform |
| title | KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping |
| title_full | KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping |
| title_fullStr | KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping |
| title_full_unstemmed | KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping |
| title_short | KEDM: Knowledge-Embedded Diffusion Model for Infrared Image Destriping |
| title_sort | kedm knowledge embedded diffusion model for infrared image destriping |
| topic | Conditional diffusion model infrared image destriping knowledge-embedded diffusion model stripe prior wavelet transform |
| url | https://ieeexplore.ieee.org/document/10978030/ |
| work_keys_str_mv | AT lingxiaoli kedmknowledgeembeddeddiffusionmodelforinfraredimagedestriping AT xinwang kedmknowledgeembeddeddiffusionmodelforinfraredimagedestriping AT danhuang kedmknowledgeembeddeddiffusionmodelforinfraredimagedestriping AT yunanhe kedmknowledgeembeddeddiffusionmodelforinfraredimagedestriping AT zhuqiangzhong kedmknowledgeembeddeddiffusionmodelforinfraredimagedestriping AT qinglingxia kedmknowledgeembeddeddiffusionmodelforinfraredimagedestriping |