{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T00:35:33Z","timestamp":1699144533337},"reference-count":30,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2023,11,1]]},"DOI":"10.1587\/transfun.2023smp0004","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T22:13:41Z","timestamp":1689891221000},"page":"1395-1405","source":"Crossref","is-referenced-by-count":0,"title":["Deep Unrolling of Non-Linear Diffusion with Extended Morphological Laplacian"],"prefix":"10.1587","volume":"E106.A","author":[{"given":"Gouki","family":"OKADA","sequence":"first","affiliation":[{"name":"Chiba Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Makoto","family":"NAKASHIZUKA","sequence":"additional","affiliation":[{"name":"Chiba Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, \u201cBeyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,\u201d IEEE Trans. Image Process., vol.26, no.7, pp.3142-3155, July 2017. 10.1109\/tip.2017.2662206","DOI":"10.1109\/TIP.2017.2662206"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] K. Zhang, W. Zuo, and L. Zhang, \u201cFFDNet: Toward a fast and flexible solution for CNN-based image denoising,\u201d IEEE Trans. Image Process., vol.27, no.9, pp.4608-4622, Sept. 2018. 10.1109\/tip.2018.2839891","DOI":"10.1109\/TIP.2018.2839891"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, and W. Gap, \u201cPre-trained image transformer,\u201d Proc. Computer Vision and Pattern Recognition, pp.12299-12310, 2021. 10.1109\/cvpr46437.2021.01212","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Y. Chen and T. Pock, \u201cTrainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.6, pp.1256-1272, June 2017. 10.1109\/tpami.2016.2596743","DOI":"10.1109\/TPAMI.2016.2596743"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] V. Monga, Y. Li, and Y.C. Eldar, \u201cAlgorithm unrolling: Interpretable, efficient deep learning for signal and image processing,\u201d IEEE Signal Process. Mag., vol.38, no.2, pp.18-44, Feb. 2021. 10.1109\/msp.2020.3016905","DOI":"10.1109\/MSP.2020.3016905"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] P. Perona and J. Malik, \u201cScale-space and edge detection using anisotropic diffusion,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.12, no.7, pp.629-639, July 1990. 10.1109\/34.56205","DOI":"10.1109\/34.56205"},{"key":"7","unstructured":"[7] J. Serra, Image Analysis and Mathematical Morphology, Academic Press, 1982."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] P. Maragos and R.W. Schafer, \u201cMorphological filters-part I: Their set-theoretic analysis and relations to linear shift-invariant filters,\u201d IEEE Trans. Acoust., Speech, Signal Process., vol.35, no.8, pp.1153-1169, Aug. 1987. 10.1109\/tassp.1987.1165259","DOI":"10.1109\/TASSP.1987.1165259"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] J. Serra, \u201cMorphological filtering: An overview,\u201d Signal Processing, vol.38, no.1, pp.3-11, July 1994. 10.1016\/0165-1684(94)90052-3","DOI":"10.1016\/0165-1684(94)90052-3"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] G. Okada and M. Nakashizuka, \u201cTrainable diffusion network based on morphological Laplacian,\u201d Proc. 2021 International Symposium on Intelligent Signal Processing and Communication Systems, pp.1-2, Taipei, Nov. 2021. 10.1109\/ispacs51563.2021.9651074","DOI":"10.1109\/ISPACS51563.2021.9651074"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] G. Okada and M. Nakashizuka, \u201cDeep unrolling of diffusion process with morphological Laplacian and its implementation with SIMD instructions,\u201d Proc. 2022 IEEE International Conference on Image Processing, pp.2931-2934, Bordeaux, Oct. 2022 10.1109\/icip46576.2022.9897852","DOI":"10.1109\/ICIP46576.2022.9897852"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] D. Mellouli, T.M. Hamdani, J.J. Sanchez-Medina, M.B. Ayed, and A.M. Alimi, \u201cMorphological convolutional neural network architecture for digit recognition,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.30, no.9, pp.2876-2885, Sept. 2019. 10.1109\/tnnls.2018.2890334","DOI":"10.1109\/TNNLS.2018.2890334"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] J. Masci, J. Angulo, and J. Schmidhuber, \u201cA learning framework for morphological operators using counter-harmonic mean,\u201d Mathematical Morphology and Its Applications to Signal and Image Processing, ISMM 2013, Lecture Notes in Computer Science, vol.7883, pp.329-340, Springer, Berlin, 2013. 10.1007\/978-3-642-38294-9_28","DOI":"10.1007\/978-3-642-38294-9_28"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] G. Franchi, A. Fehri, and A. Yao, \u201cDeep morphological networks,\u201d Pattern Recognition, vol.102, pp.1-11, Jan. 2020. 10.1016\/j.patcog.2020.107246","DOI":"10.1016\/j.patcog.2020.107246"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] K. Nogueira, J. Chanussot, M.D. Mura, and J.A. Dos Santos, \u201cAn introduction to deep morphological networks,\u201d IEEE Access, vol.9, pp.114308-114324, 2021. 10.1109\/access.2021.3104405","DOI":"10.1109\/ACCESS.2021.3104405"},{"key":"16","unstructured":"[16] I.J. Goodfellow, D. Warde-Farley, A. Courville, and Y. Bengio, \u201cMaxout networks,\u201d Proc. 30th Int. Conf. on Machine Learning, 2013."},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] M. Nakashizuka, K. Kobayashi, T. Ishikawa, and K. Itoi, \u201cConvex filter networks based on morphological filters and their application to image noise and mask removal,\u201d IEICE Trans. Fundamentals, vol.E100-A, no.11, pp.2238-2247, Nov. 2017. 10.1587\/transfun.e100.a.2238","DOI":"10.1587\/transfun.E100.A.2238"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] H. Fujisaki and M. Nakashizuka, \u201cDeep morphological filter networks for Gaussian denoising,\u201d Proc. 2020 IEEE International Conference on Image Processing, pp.918-922, Abu Dhabi, Oct. 2020. 10.1109\/icip40778.2020.9190657","DOI":"10.1109\/ICIP40778.2020.9190657"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] H. Fujisaki and M, Nakashizuka, \u201cDeep Gaussian denoising network based on morphological operators with low-precision arithmetic,\u201d IEICE Trans. Fundamentals, vol.E105-A, no.4, pp.631-638, April 2022. 10.1587\/transfun.2021smp0008","DOI":"10.1587\/transfun.2021SMP0008"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, Las Vegas, June 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"21","unstructured":"[21] Arm Neon, https:\/\/www.arm.com\/ja\/why-arm\/technologies\/neon"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] C. Guillemot and O.L. Meur, \u201cImage inpainting,\u201d IEEE Signal Process. Mag., vol.31, no.1, pp.127-144, Jan. 2014. 10.1109\/msp.2013.2273004","DOI":"10.1109\/MSP.2013.2273004"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J.F. Rivert, P. Soulle, and S. Beucher, \u201cMorphological gradients,\u201d J. Electron. Imaging, vol.2, no.4, pp.326-336, Oct. 1993. 10.1117\/12.159642","DOI":"10.1117\/12.159642"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] P. Kuosmanen and J. Astola, \u201cSoft morphological filtering,\u201d J. Math. Imaging Vis., vol.5, pp.231-262, 1995. 10.1007\/bf01248374","DOI":"10.1007\/BF01248374"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. Huang, \u201cFree-form image inpainting with gated convolution,\u201d Proc. 2019 International Conference on Computer Vision, 2019. 10.1109\/iccv.2019.00457","DOI":"10.1109\/ICCV.2019.00457"},{"key":"26","unstructured":"[26] J. Duchi, E. Hazan, and Y. Singer, \u201cAdaptive subgradient methods for online learning and stochastic optimization,\u201d J. Machine Learning Res., no.12, pp.2121-2159, 2011."},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, \u201cImage denosing by sparse 3-D transform-domain collaborative filtering,\u201d IEEE Trans. Image Process., vol.16, no.8, pp.2080-2095, Aug. 2007. 10.1109\/tip.2007.901238","DOI":"10.1109\/TIP.2007.901238"},{"key":"28","unstructured":"[28] The Berkeley Segmentation Dataset and Benchmark, https:\/\/www2.eecs.berkeley.edu\/Research\/Projects\/CS\/vision\/bsds\/"},{"key":"29","unstructured":"[29] https:\/\/www.tensorflow.org"},{"key":"30","unstructured":"[30] Raspberry Pi 4 model B, https:\/\/www.raspberrypi.org\/products\/raspberry-pi-4-model-b\/"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/11\/E106.A_2023SMP0004\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T03:18:15Z","timestamp":1699067895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/11\/E106.A_2023SMP0004\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":30,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2023smp0004","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]},"article-number":"2023SMP0004"}}