Abstract
The fusion of input and guidance images that have a tradeoff in their information (e.g., hyperspectral and RGB image fusion or pansharpening) can be interpreted as one general problem. However, previous studies applied a task-specific handcrafted prior and did not address the problems with a unified approach. To address this limitation, in this study, we propose a guided deep decoder network as a general prior. The proposed network is composed of an encoder-decoder network that exploits multi-scale features of a guidance image and a deep decoder network that generates an output image. The two networks are connected by feature refinement units to embed the multi-scale features of the guidance image into the deep decoder network. The proposed network allows the network parameters to be optimized in an unsupervised way without training data. Our results show that the proposed network can achieve state-of-the-art performance in various image fusion problems.
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References
Akhtar, N., Shafait, F., Mian, A.: Bayesian sparse representation for hyperspectral image super resolution. In: CVPR, pp. 3631–3640 (2015)
Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., Nencini, F., Selva, M.: Multispectral and panchromatic data fusion assessment without reference. Photogramm. Eng. Remote Sens. 74(2), 193–200 (2008)
Alparone, L., Baronti, S., Garzelli, A., Nencini, F.: A global quality measurement of pan-sharpened multispectral imagery. IEEE Geosci. Remote Sens. Lett. 1(4), 313–317 (2004)
Chakrabarty, P., Maji, S.: The spectral bias of the deep image prior. In: NeurIPS Workshops (2019)
Chen, C., Li, Y., Liu, W., Huang, Z.: SIRF: simultaneous satellite image registration and fusion in a unified framework. IEEE Trans. Image Process. 24(11), 4213–4224 (2015)
Chen, C., Li, Y., Liu, W., Huang, J.: Image fusion with local spectral consistency and dynamic gradient sparsity. In: CVPR (2014)
Dian, R., Fang, L., Li, S.: Hyperspectral image super-resolution via non-local sparse tensor factorization. In: CVPR, pp. 3862–3871 (2017)
Dian, R., Li, S., Guo, A., Fang, L.: Deep hyperspectral image sharpening. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5345–5355 (2018)
Dong, W., et al.: Hyperspectral image super-resolution via non-negative structured sparse representation. IEEE Transactions on Image Processing 25(5) (2016)
Fu, X., Lin, Z., Huang, Y., Ding, X.: A variational pan-sharpening with local gradient constraints. In: CVPR (2019)
Fu, Y., Zhang, T., Zheng, Y., Zhang, D., Huang, H.: Hyperspectral image super-resolution with optimized rgb guidance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11661–11670 (2019)
Gandelsman, Y., Shocher, A., Irani, M.: “Double-Dip”: unsupervised image decomposition via coupled deep-image-priors. In: CVPR (2019)
Garzelli, A., Nencini, F., Capobianco, L.: Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Trans. Geosci. Remote Sens. 46(1), 228–236 (2007)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Heckel, R., Hand, P.: Deep decoder: Concise image representations from untrained non-convolutional networks. In: ICLR (2019)
Kawakami, R., Matsushita, Y., Wright, J., Ben-Ezra, M., Tai, Y., Ikeuchi, K.: High-resolution hyperspectral imaging via matrix factorization. In: CVPR, pp. 2329–2336 (2011)
Kwon, H., Tai, Y.W.: RGB-guided hyperspectral image upsampling. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 307–315 (2015)
Lanaras, C., Baltsavias, E., Schindler, K.: Hyperspectral super-resolution by coupled spectral unmixing. In: ICCV (2015)
Liu, P., Xiao, L., Li, T.: A variational pan-sharpening method based on spatial fractional-order geometry and spectral–spatial low-rank priors. IEEE Trans. Geosci. Remote Sens. 56, 1788–1802 (2018)
Lutio, R.d., D’Aronco, S., Wegner, J.D., Schindler, K.: Guided super-resolution as pixel-to-pixel transformation. In: ICCV (2019)
Masi, G., Cozzolino, D., Verdoliva, L., Scarpa, G.: Pansharpening by convolutional neural networks. Remote Sensing 8(7), 594 (2016)
Palsson, F., Sveinsson, J.R., Ulfarsson, M.O.: A new pansharpening algorithm based on total variation. IEEE Geosci. Remote Sens. Lett. 11, 318–322 (2014)
Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 23(3), 664 (2004)
Qu, Y., Qi, H., Kwan, C.: Unsupervised sparse Dirichlet-net for hyperspectral image super-resolution. In: CVPR (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Scarpa, G., Vitale, S., Cozzolino, D.: Target-adaptive CNN-based pansharpening. IEEE Trans. Geosci. Remote Sens. 56(9), 5443–5457 (2018)
Sidorov, O., Hardeberg, J.Y.: Deep hyperspectral prior: denoising, inpainting, super-resolution. In: ICIP (2019)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: CVPR (2018)
Vivone, G., et al.: A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 53(5), 2565–2586 (2014)
Wald, L., Ranchin, T., Mangolini, M.: Fusion of satellite images of different spatial resoltuions: assessing the quality of resulting images. Photogrammetric engineering and remote sensing 63(6), 691–699 (1997)
Wald, L.: Quality of high resolution synthesised images: is there a simple criterion? In: Third Conference Fusion of Earth Data: Merging Point Measurements, Raster Maps and Remotely Sensed Images, pp. 99–103. SEE/URISCA (2000)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wei, Q., Dobigeon, N., Tourneret, J., Bioucas-Dias, J., Godsill, S.: R-FUSE: robust fast fusion of multiband images based on solving a Sylvester equation. IEEE Signal Process. Lett. 23(11), 1632–1636 (2016)
Wei, Y., Yuan, Q., Shen, H., Zhang, L.: Boosting the accuracy of multispectral image pansharpening by learning a deep residual network. IEEE Geosci. Remote Sens. Lett. 14(10), 1795–1799 (2017)
Xie, Q., Zhou, M., Zhao, Q., Meng, D., Zuo, W., Xu, Z.: Multispectral and hyperspectral image fusion by MS/HS fusion net. In: CVPR (2019)
Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., Paisley, J.: PanNet: a deep network architecture for pan-sharpening. In: ICCV. pp. 1753–1761 (2017)
Yokota, T., Kawai, K., Sakata, M., Kimura, Y., Hontani, H.: Dynamic pet image reconstruction using nonnegative matrix factorization incorporated with deep image prior. In: ICCV (2019)
Yokoya, N., Grohnfeldt, C., Chanussot, J.: Hyperspectral and multispectral data fusion: a comparative review of the recent literature. IEEE Geosci. Remote Sens. Mag. 5(2), 29–56 (2017)
Yokoya, N., Yairi, T., Iwasaki, A.: Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Trans. Geosci. Remote Sens. 50(2), 528–537 (2012)
Zhou, J., Civco, D., Silander, J.: A wavelet transform method to merge landsat TM and SPOT panchromatic data. Int. J. Remote Sens. 19(4), 743–757 (1998)
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Uezato, T., Hong, D., Yokoya, N., He, W. (2020). Guided Deep Decoder: Unsupervised Image Pair Fusion. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_6
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