1,797
Views
6
CrossRef citations to date
0
Altmetric
Advanced Machine Learning and Optimization Theories and Algorithms for Heterogeneous Data Analytics

Deep blur detection network with boundary-aware multi-scale features

ORCID Icon, , , &
Pages 766-784 | Received 23 Jan 2021, Accepted 15 May 2021, Published online: 02 Jun 2021

References

  • Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
  • Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009). Frequency-tuned salient region detection. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1597–1604). https://doi.org/10.1109/CVPR.2009.5206596.
  • Bae, S., & Durand, F. (2007). Defocus magnification. Computer Graphics Forum, 26(3), 571–579. https://doi.org/10.1111/cgf.2007.26.issue-3
  • Chakrabarti, A., Zickler, T., & Freeman, W. T. (2010). Analyzing spatially-varying blur. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 2512–2519). https://doi.org/10.1109/CVPR.2010.5539954.
  • Chen, Y. H., Chang, C. C., & Hsu, C. Y. (2020). Content-based image retrieval using block truncation coding based on edge quantization. Connection Science, 32(4), 431–448. https://doi.org/10.1080/09540091.2020.1753174
  • Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295–307. https://doi.org/10.1109/TPAMI.2015.2439281
  • Elder, J. H., & Zucker, S. W. (1998). Local scale control for edge detection and blur estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(7), 699–716. https://doi.org/10.1109/34.689301
  • Golestaneh, S. A., & Karam, L. J. (2017). Spatially-varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 596–605). https://doi.org/10.1109/CVPR.2017.71.
  • Honari, S., Yosinski, J., Vincent, P., & Pal, C. (2016). Recombinator networks: Learning coarse-to-fine feature aggregation. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 5743–5752). https://doi.org/10.1109/CVPR.2016.619.
  • Jiang, P., Ling, H., Yu, J., & Peng, J. (2013). Salient region detection by ufo: Uniqueness, focusness and objectness. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1976–1983). https://doi.org/10.1109/ICCV.2013.248.
  • Kang, K., Ouyang, W., Li, H., & Wang, X. (2016). Object detection from video tubelets with convolutional neural networks. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 817–825). https://doi.org/10.1109/CVPR.2016.95.
  • Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations 2015. https://de.arxiv.org/pdf/1412.6980.
  • Levin, A. (2007). Blind motion deblurring using image statistics. In Proceedings of Advances in Neural Information Processing Systems (pp. 841–848). https://dl.acm.org/doi/10.5555/2976456.2976562.
  • Liu, R., Li, Z., & Jia, J. (2008). Image partial blur detection and classification. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1–8). https://doi.org/10.1109/CVPR.2008.4587465.
  • Liu, H. Y., Wang, Y. P., & Fan, N. L. (2020). A hybrid deep grouping algorithm for large scale global optimization. IEEE Transactions on Evolutionary Computation, 24(6), 1112–1124. https://doi.org/10.1109/TEVC.4235
  • Lu, C., Shi, J., & Jia, J. (2013). Abnormal event detection at 150 fps in Matlab. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2720–2727). https://doi.org/10.1109/ICCV.2013.338.
  • Lu, Y., Stafford, T., & Fox, C. (2016). Maximum saliency bias in binocular fusion. Connection Science, 28(3), 258–269. https://doi.org/10.1080/09540091.2016.1159181
  • Ma, K., Fu, H., Liu, T., Wang, Z., & Tao, D. (2018). Deep blur mapping: Exploiting high-level semantics by deep neural networks. IEEE Transactions on Image Processing, 27(10), 5155–5166. https://doi.org/10.1109/TIP.83
  • Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In Proceedings of the IEEE International Conference on 3d Vision (pp. 565–571). https://doi.org/10.1109/3DV.2016.79.
  • Newell, A., Yang, K., & Deng, J. (2016). Stacked hourglass networks for human pose estimation. In Proceedings of the European Conference on Computer Vision (pp. 483–499). https://doi.org/10.1007/978-3-319-46484-8_29.
  • Park, J., Tai, Y. W., Cho, D., & So Kweon, I. (2017). A unified approach of multi-scale deep and hand-crafted features for defocus estimation. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1736–1745). https://doi.org/10.1109/CVPR.2017.295.
  • Pinheiro, P. O., Lin, T. Y., Collobert, R., & Dollár, P. (2016). Learning to refine object segments. In Proceedings of European Conference on Computer Vision (pp. 75–91). https://doi.org/10.1007/978-3-319-46448-0_5.
  • Shi, J., Xu, L., & Jia, J. (2014). Discriminative blur detection features. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 2965–2972). https://doi.org/10.1109/CVPR.2014.379.
  • Shi, J., Xu, L., & Jia, J. (2015). Just noticeable defocus blur detection and estimation. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 657–665). https://doi.org/10.1109/CVPR.2015.7298665.
  • Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations 2015. https://arxiv.org/abs/1409.1556.
  • Srivastava, V., & Biswas, B. (2020). CNN-based salient features in HSI image semantic target prediction. Connection Science, 32(2), 113–131. https://doi.org/10.1080/09540091.2019.1650330
  • Su, B., Lu, S., & Tan, C. L. (2011). Blurred image region detection and classification. In Proceedings of the ACM International Conference on Multimedia (pp. 1397–1400). https://dl.acm.org/doi/10.5555/1785794.1785825.
  • Sun, C., Wang, D., Lu, H., & Yang, M. H. (2018). Learning spatial-aware regressions for visual tracking. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 8962–8970). https://doi.org/10.1109/CVPR.2018.00934.
  • Sun, X., Zhang, X., Zou, W., & Xu, C. (2017). Focus prior estimation for salient object detection. In Proceedings of the IEEE International Conference on Image Processing (pp. 1532–1536). https://doi.org/10.1109/ICIP.2017.8296538.
  • Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging, 15(1), 29. https://doi.org/10.1186/s12880-015-0068-x
  • Tai, Y. W., & Brown, M. S. (2009). Single image defocus map estimation using local contrast prior. In Proceedings of the IEEE International Conference on Image Processing (pp. 1797–1800). https://doi.org/10.1109/ICIP.2009.5414620.
  • Tang, C., Hou, C., & Song, Z. (2013). Defocus map estimation from a single image via spectrum contrast. Optics Letters, 38(10), 1706–1708. https://doi.org/10.1364/OL.38.001706
  • Tang, C., Wu, J., Hou, Y., Wang, P., & Li, W. (2016). A spectral and spatial approach of coarse-to-fine blurred image region detection. IEEE Signal Processing Letters, 23(11), 1652–1656. https://doi.org/10.1109/LSP.2016.2611608
  • Vu, C. T., Phan, T. D., & Chandler, D. M. (2011). A spectral and spatial measure of local perceived sharpness in natural images. IEEE Transactions on Image Processing, 21(3), 934–945. https://doi.org/10.1109/TIP.2011.2169974
  • Wang, Y. W., Liu, H., Wei, F., Zong, T., & Li, X. (2016). Cooperativeco-evolution with formula-based variable grouping for large-scale global optimization. Evolutionary Computation, 26(4), 569–596. https://doi.org/10.1162/evco_a_00214
  • Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2015). HCP: A flexible CNN framework for multi-label image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1901–1907. https://doi.org/10.1109/TPAMI.2015.2491929
  • Wu, Z., Gao, Y., Li, L., Xue, J., & Li, Y. (2019). Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold. Connection Science, 31(2), 169–184. https://doi.org/10.1080/09540091.2018.1510902
  • Xu, Z., & Zhang, W. (2020). Hand segmentation pipeline from depth map: an integrated approach of histogram threshold selection and shallow CNN classification. Connection Science, 32(2), 162–173. https://doi.org/10.1080/09540091.2019.1670621
  • Xue, X., & Wang, Y. (2015). Optimizing ontology alignments through a memetic algorithm using both match fmeasure and unanimous improvement ratio. Artificial Intelligence, 223, 65–81. https://doi.org/10.1016/j.artint.2015.03.001
  • Xue, X., & Wang, Y. (2016). Using memetic algorithm for instance coreference resolution. IEEE Transactions on Knowledge and Data Engineering, 28(2), 580–591. https://doi.org/10.1109/TKDE.2015.2475755
  • Yan, Q., Xu, L., Shi, J., & Jia, J. (2013). Hierarchical saliency detection. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1155–1162). https://doi.org/10.1109/CVPR.2013.153.
  • Ye, M., Wang, Y., Dai, C., & Wang, X. (2016). A hybrid genetic algorithm for minimum exposure path problem of wireless sensor network based on a numerical functional extreme model. IEEE Transactions on Vehicular Technology, 65(10), 8644–8657. https://doi.org/10.1109/TVT.2015.2508504
  • Yi, X., & Eramian, M. (2016). LBP-based segmentation of defocus blur. IEEE Transactions on Image Processing, 25(4), 1626–1638. https://doi.org/10.1109/TIP.2016.2528042
  • Zhang, X., Wang, R., Jiang, X., Wang, W., & Gao, W. (2016). Spatially variant defocus blur map estimation and deblurring from a single image. Journal of Visual Communication and Image Representation, 35, 257–264. https://doi.org/10.1016/j.jvcir.2016.01.002
  • Zhang, P., Wang, D., Lu, H., Wang, H., & Yin, B. (2017). Learning uncertain convolutional features for accurate saliency detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 212–221). https://doi.org/10.1109/ICCV.2017.32.
  • Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. https://doi.org/10.1109/TIP.83
  • Zhao, J., Feng, H., Xu, Z., Li, Q., & Tao, X. (2013). Automatic blur region segmentation approach using image matting. Signal, Image and Video Processing, 7(6), 1173–1181. https://doi.org/10.1007/s11760-012-0381-6
  • Zhu, X., Zuo, J., & Ren, H. (2020). A modified deep neural network enables identification of foliage under complex background. Connection Science, 32(1), 1–15. https://doi.org/10.1080/09540091.2019.1609420
  • Zhuo, S., & Sim, T. (2011). Defocus map estimation from a single image. Pattern Recognition, 44(9), 1852–1858. https://doi.org/10.1016/j.patcog.2011.03.009
  • Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., Wells III, W. M., Jolesz, F. A., & Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index. Academic Radiology, 11(2), 178–189. https://doi.org/10.1016/S1076-6332(03)00671-8