Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2410004(2022)
Multi-Focus Image Fusion Method Based on Cooperative Detection via a Deep Dense Convolutional Neural Network
Figures & Tables(8)
Fig. 1. Overall framework of proposed fusion method. (a) Collaborative detection via densely connected convolutional neural networks; (b) multi-scale information extraction for pyramid pooling network
Fig. 3. Probabilistic decision diagram for multi-scale pyramid pooling collaborative detection. (a) Source image; (b) rough segmentation; (c) refined segmentation
Fig. 4. Lytro-3 image fusion results. A is corresponding fusion results obtained by various methods, and B is difference images in pseudo color map form obtained by subtracting source image A from each fused image
Fig. 5. Lytro-17 image fusion results. A is corresponding fusion results obtained by various methods, and B is difference images in pseudo color map form obtained by subtracting source image A from each fused image
Fig. 6. More results of proposed method. The first to fourth and fifth to eighth columns show fusion results of 18 pairs of multi-focus images, respectively
Table 1. Comparison of objective evaluation of fusion results
View tableTable 1. Comparison of objective evaluation of fusion results
Image Metrics Fusion method NSCT GFF IFM CBF DCHWT CNN PSPF Proposed Lytro-1 QMI 2.8165 3.6591 3.7414 3.3332 2.9543 3.7935 3.9901 4.0177 QNCIE 0.8193 0.8301 0.8313 0.8255 0.8208 0.8318 0.8349 0.8354 QAB/F 0.6004 0.7531 0.7405 0.7418 0.6945 0.7539 0.7560 0.7575 QVIF 0.5432 0.7461 0.7037 0.6863 0.6476 0.7476 0.7452 0.7455 Lytro-3 QMI 2.8810 3.8132 4.0430 3.4832 3.1244 4.0501 4.1158 4.1351 QNCIE 0.8203 0.8320 0.8355 0.8273 0.8229 0.8356 0.8368 0.8370 QAB/F 0.5497 0.7437 0.7432 0.7311 0.6964 0.7466 0.7415 0.7455 QVIF 0.4804 0.7546 0.7518 0.6936 0.6484 0.7593 0.7512 0.7571 Lytro-9 QMI 2.5304 3.8522 4.0807 3.6374 2.9118 4.0713 4.1162 4.1549 QNCIE 0.8160 0.8319 0.8356 0.8288 0.8199 0.8355 0.8363 0.8367 QAB/F 0.4625 0.8004 0.7992 0.7919 0.7448 0.8015 0.8010 0.8017 QVIF 0.4193 0.7760 0.7703 0.7337 0.6546 0.7766 0.7793 0.7753 Lytro-17 QMI 2.4034 3.4225 3.6484 3.0606 2.7818 3.7062 3.8529 3.8604 QNCIE 0.8143 0.8275 0.8301 0.8229 0.8191 0.8313 0.8328 0.8337 QAB/F 0.5400 0.7660 0.7547 0.7529 0.7297 0.7695 0.7677 0.7693 QVIF 0.4701 0.7123 0.6971 0.6496 0.6148 0.7227 0.7250 0.7270 Lytro-18 QMI 4.0753 4.8021 4.8594 4.5228 4.2013 4.9014 4.8296 4.9635 QNCIE 0.8403 0.8525 0.8537 0.8476 0.8422 0.8545 0.8531 0.8557 QAB/F 0.6282 0.7397 0.7352 0.7285 0.6877 0.7414 0.7332 0.7425 QVIF 0.6226 0.8049 0.7916 0.7441 0.7232 0.8086 0.7891 0.7982
Table 2. Comparison of average objective evaluation of fusion results
View tableTable 2. Comparison of average objective evaluation of fusion results
Metrics Fusion method NSCT GFF IFM CBF DCHWT CNN PSPF Proposed QMI 3.1473 4.1211 4.2879 3.8211 3.3649 4.3210 4.4357 4.4656 QNCIE 0.8250 0.8395 0.8425 0.8344 0.8278 0.8430 0.8449 0.8459 QAB/F 0.5799 0.7601 0.7534 0.7528 0.7124 0.7618 0.7590 0.7669 QVIF 0.5132 0.7430 0.7233 0.6870 0.6465 0.7465 0.7406 0.7435
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Wei Yang, Liye Mei, Chuan Xu, Huan Zhang, Chuanwen Hu, Qianchuang Deng. Multi-Focus Image Fusion Method Based on Cooperative Detection via a Deep Dense Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2410004
Paper Information
Category: Image Processing
Received: Sep. 13, 2021
Accepted: Oct. 27, 2021
Published Online: Jan. 11, 2023
The Author Email: Chuanwen Hu (Chuanwenhu@qq.com)
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