Noise Reduction and Temperature Field Reconstruction of Flame Light Field Images Based on Improved U-network

SHAN Liang; SUN Jian; HONG Bo; KONG Ming

doi:10.11999/JEIT240836

Volume 47 Issue 3

Mar. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(3): 792-802

SHAN Liang, SUN Jian, HONG Bo, KONG Ming. Noise Reduction and Temperature Field Reconstruction of Flame Light Field Images Based on Improved U-network[J]. Journal of Electronics & Information Technology, 2025, 47(3): 792-802. doi: 10.11999/JEIT240836

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SHAN Liang, SUN Jian, HONG Bo, KONG Ming. Noise Reduction and Temperature Field Reconstruction of Flame Light Field Images Based on Improved U-network[J]. Journal of Electronics & Information Technology, 2025, 47(3): 792-802. doi: 10.11999/JEIT240836

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Noise Reduction and Temperature Field Reconstruction of Flame Light Field Images Based on Improved U-network

doi: 10.11999/JEIT240836 cstr: 32379.14.JEIT240836

SHAN Liang¹,
SUN Jian¹,
HONG Bo¹,
KONG Ming^{2
,
,}

1.
Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China
2.
College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China (51874264, 52076200), The Central Guiding Local Science and Technology Development Fund Projects of China (2023ZY1008)

Received Date: 2024-10-08
Rev Recd Date: 2025-02-22

Available Online: 2025-03-05

Publish Date: 2025-03-01

Abstract

Abstract

Objective This study establishes the nonlinear relationship between flame light field images and the 3D temperature field using deep learning techniques, enabling rapid 3D reconstruction of the flame temperature field. However, light-field images are prone to radiation and imaging noise during transmission and imaging, which significantly degrades image quality and reduces the accuracy of temperature field reconstruction. Therefore, denoising of flame light field images, with maximum preservation of texture and edge details, is critical for high-precision 3D reconstruction. Deep learning-based denoising algorithms are capable of accommodating a broad range of noise distributions and are particularly effective in enhancing texture and contour information without requiring extensive prior knowledge. Given the complexity of noise in flame light field images, deep learning methods present an optimal solution for denoising. Methods This paper presents a denoising model based on an improved UNet network, designed to address radiation and imaging noise, as well as the texture information in complex flame images. The model reduces noise and optimizes the flame light field image through three modules: the background purification module, the UNet denoising module, and the edge optimization module. Feature extraction is performed on the image background layer using dense convolution operations, with a focus on purifying the radiated noise embedded in the background. The symmetrical encoder-decoder network structure and skip connections in the UNet module help to reduce both radiation noise between channels and imaging noise on the surface. The edge optimization module is tailored to extract detailed information from the image, aiming to enhance the quality of the flame light field images. Comparative and ablation experiments confirm the superior noise reduction performance and effectiveness of the proposed modules. Results and Discussions In the numerical simulation, radiation noise and imaging noise are added to the flame light field image, generating three types of datasets: single radiation noise, single imaging noise, and mixed noise. In the denoising experiment, the BUE denoising model is compared with UNet, CBDNet, DnCNN, and BRDNet. The denoising results (Fig. 4) show that the PSNR and SSIM values of our BUE model exceed those of the other models, reaching 47 dB and 0.9931, respectively. Analysis of the four denoised texture images (Fig. 5) demonstrates that the BUE model effectively removes background noise while preserving internal details, such as texture and contour features. Ablation experiments are also conducted by adding the BPM and EIEM modules to the UNet benchmark model. The experimental results (Fig. 5, Fig. 6) confirm the effectiveness of the BPM and EIEM modules. Subsequently, the flame light field image is denoised using the proposed model, followed by reconstruction of the temperature field (Fig. 8). The average relative error of the reconstruction is reduced by approximately 37% to 57% compared to the non-denoised case, significantly improving the accuracy of the 3D flame temperature field reconstruction. In the real-world experiment, light field images of a candle flame and a butane flame are obtained. The SSIM values after denoising using the BUE model are 0.9870 and 0.9808, respectively. Conclusions This paper presents a BUE denoising method based on the UNet model, incorporating a background purification module and an edge information enhancement module. This approach effectively extracts the background, reduces noise, and enhances contour and texture details in noisy flame light field images. The noise reduction performance of the model is evaluated through numerical simulations, and the results demonstrate the following: (1) Compared to UNet, CBDNet, DnCNN, and BRDNet, the proposed BUE denoising model shows significant advantages. Under mixed noise conditions with a signal-to-noise ratio of 10 dB, the model achieves a PSNR of 47 dB and an SSIM of 0.9931. Specifically, the PSNR improves by approximately 23.68% compared to UNet and 4.44% compared to DnCNN. (2) By integrating BUE as a denoising preprocessing module into the temperature field reconstruction model, the results show that incorporating denoising reduces the average relative error by approximately 37% to 57% compared to reconstruction without denoising. (3) Real candle flame and butane flame light field images are acquired, and the proposed noise reduction model achieves SSIM values of 0.9870 for the candle flame image and 0.9808 for the butane flame image after denoising.
- Image processing,
- Image noise reduction,
- Deep learning,
- Flame light field images,
- 3D temperature field reconstruction

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References(26)

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