Laser & Optoelectronics Progress, Volume. 61, Issue 4, 0437009(2024)
Tone Mapping Algorithm for High Dynamic Range Images Based on Improved Laplacian Pyramid
Fig. 1. Network architecture of tone mapping algorithm
Fig. 2. Schematic diagram of improved Laplacian pyramid
Fig. 3. Adaptive group convolution module
Fig. 4. Visual perception experiment setting
Fig. 5. Comparison of the algorithms effect
Fig. 6. Mean scores of image quality of LDR images in each group
Fig. 7. Network structure of ablation experiment. (a) Without AGCM module; (b) only global extraction network contains AGCM module; (c) only fine tone network contains AGCM module
Fig. 8. Ablation experiment of subnets. (a) Only contain global extraction network; (b) only contain local adjustment network; (c) only contain fine tone network; (d) without global extraction network; (e) without local adjustment network; (f) without fine tone network; (g) whole network
Fig. 9. Parametric experimental results of α and β. (a) Value of TMQI corresponding to the parameter combination; (b) value of FSITM corresponding to the parameter combination; (c) value of HDR-VDP2 corresponding to the parameter combination
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Bowen Zhang, Zhenping Xia, Yueyuan Zhang, Cheng Cheng, Yujie Liu. Tone Mapping Algorithm for High Dynamic Range Images Based on Improved Laplacian Pyramid[J]. Laser & Optoelectronics Progress, 2024, 61(4): 0437009
Category: Digital Image Processing
Received: Feb. 6, 2023
Accepted: Mar. 10, 2023
Published Online: Feb. 26, 2024
The Author Email: Xia Zhenping (xzp@usts.edu.cn)