Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model
The generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their applicability on devices with moderate resources. I...
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MDPI AG
2025-04-01
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| author | Gustavo de Souza Ferreti Thuanne Paixão Ana Beatriz Alvarez |
| author_facet | Gustavo de Souza Ferreti Thuanne Paixão Ana Beatriz Alvarez |
| author_sort | Gustavo de Souza Ferreti |
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| description | The generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their applicability on devices with moderate resources. In this context, this paper presents a lightweight architecture for reconstructing HDR images from three Low-Dynamic-Range inputs. The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. The evaluation of the proposal is conducted through quantitative, qualitative, and computational cost analyses based on the number of parameters and FLOPs. Regarding the qualitative analysis, a comparison between the models was performed using samples that present reconstruction challenges. The proposed model achieves a PSNR-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> of 43.51 dB and SSIM-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> of 0.9917, achieving competitive quality metrics comparable to HDR-GAN while reducing the computational cost by 6× in FLOPs and 7× in the number of parameters, using approximately half the GPU memory consumption, demonstrating an effective balance between visual fidelity and efficiency. |
| format | Article |
| id | doaj-art-704f087275b84dbf9260fccc7d9707c2 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-704f087275b84dbf9260fccc7d9707c22025-08-20T01:49:27ZengMDPI AGApplied Sciences2076-34172025-04-01159480110.3390/app15094801Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction ModelGustavo de Souza Ferreti0Thuanne Paixão1Ana Beatriz Alvarez2PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilPAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilPAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, BrazilThe generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their applicability on devices with moderate resources. In this context, this paper presents a lightweight architecture for reconstructing HDR images from three Low-Dynamic-Range inputs. The proposed model is based on Generative Adversarial Networks and replaces traditional convolutions with depthwise separable convolutions, reducing the number of parameters while maintaining high visual quality and minimizing luminance artifacts. The evaluation of the proposal is conducted through quantitative, qualitative, and computational cost analyses based on the number of parameters and FLOPs. Regarding the qualitative analysis, a comparison between the models was performed using samples that present reconstruction challenges. The proposed model achieves a PSNR-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> of 43.51 dB and SSIM-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> of 0.9917, achieving competitive quality metrics comparable to HDR-GAN while reducing the computational cost by 6× in FLOPs and 7× in the number of parameters, using approximately half the GPU memory consumption, demonstrating an effective balance between visual fidelity and efficiency.https://www.mdpi.com/2076-3417/15/9/4801HDR reconstructionmulti-exposed imaginglarge motiondepthwise separable convolutiongenerative adversarial network |
| spellingShingle | Gustavo de Souza Ferreti Thuanne Paixão Ana Beatriz Alvarez Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model Applied Sciences HDR reconstruction multi-exposed imaging large motion depthwise separable convolution generative adversarial network |
| title | Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model |
| title_full | Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model |
| title_fullStr | Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model |
| title_full_unstemmed | Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model |
| title_short | Generative Adversarial Network-Based Lightweight High-Dynamic-Range Image Reconstruction Model |
| title_sort | generative adversarial network based lightweight high dynamic range image reconstruction model |
| topic | HDR reconstruction multi-exposed imaging large motion depthwise separable convolution generative adversarial network |
| url | https://www.mdpi.com/2076-3417/15/9/4801 |
| work_keys_str_mv | AT gustavodesouzaferreti generativeadversarialnetworkbasedlightweighthighdynamicrangeimagereconstructionmodel AT thuannepaixao generativeadversarialnetworkbasedlightweighthighdynamicrangeimagereconstructionmodel AT anabeatrizalvarez generativeadversarialnetworkbasedlightweighthighdynamicrangeimagereconstructionmodel |