Abstract
Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world. To tackle this issue, we present a Universal and Recognition-friendly Image Enhancement network, dubbed URIE, which is attached in front of existing recognition models and enhances distorted input to improve their performance without retraining them. URIE is universal in that it aims to handle various factors of image degradation and to be incorporated with any arbitrary recognition models. Also, it is recognition-friendly since it is optimized to improve the robustness of following recognition models, instead of perceptual quality of output image. Our experiments demonstrate that URIE can handle various and latent image distortions and improve the performance of existing models for five diverse recognition tasks where input images are degraded.
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Notes
- 1.
Gaussian noise, shot noise, impulse noise, defocus blur, glass blur, motion blur, zoom blur, snow, frost, fog, brightness, contrast, elastic transform, pixelation, jpeg.
- 2.
Speckle noise, Gaussian blur, spatter, saturation.
References
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of European Conference on Computer Vision (ECCV) (2018)
Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster r-cnn for object detection in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)
Diamond, S., Sitzmann, V., Boyd, S.P., Wetzstein, G., Heide, F.: Dirty pixels: Optimizing image classification architectures for raw sensor data. arXiv preprint arXiv:1701.06487 (2017)
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell (TPAMI) 38(2), 295–307 (2016)
Everingham, M., et al.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-4
Geirhos, R., Temme, C.R.M., Rauber, J., Schütt, H.H., Bethge, M., Wichmann, F.A.: Generalisation in humans and deep neural networks. In: Proceedings of Neural Information Processing Systems (NeurIPS) (2018)
Gomez, R., Zhang, Z., González-Jiménez, J., Scaramuzza, D.: Learning-based image enhancement for visual odometry in challenging hdr environments. In: Proceedings of International Conference on Robatics and Automation (ICRA) (2018)
Gopalan, R., Taheri, S., Turaga, P., Chellappa, R.: A blur-robust descriptor with applications to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 34(6), 1220–1226 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: Proceedings of International Conference on Learning Representations (ICLR) (2019)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of International Conference on Machine Learning (ICML) (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of International Conference on Learning Representations (ICLR) (2015)
Lee, S., et al.: VPGNet: vanishing point guided network for lane and road marking detection and recognition. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2017)
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2017)
Li, S., et al.: Single image deraining: a comprehensive benchmark analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019) 9
Liu, D., Wen, B., Fan, Y., Loy, C.C., Huang, T.S.: Non-local recurrent network for image restoration. In: Proceedings of Neural Information Processing Systems (NeurIPS) (2018)
Liu, D., Wen, B., Liu, X., Wang, Z., Huang, T.S.: When image denoising meets high-level vision tasks: a deep learning approach. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) (2018)
Liu, W., et al.: SSD: Single shot multibox detector. In: Proceedings of European Conference on Computer Vision (ECCV) (2016)
Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2015)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Pei, Y., Huang, Y., Zou, Q., Lu, Y., Wang, S.: Does haze removal help cnn-based image classification? In: Proceedings of European Conference on Computer Vision (ECCV) (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2015)
Sakaridis, C., Dai, D., Gool, L.V.: Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2019)
Sakaridis, C., Dai, D., Hecker, S., Van Gool, L.: Model adaptation with synthetic and real data for semantic dense foggy scene understanding. In: Proceedings of European Conference on Computer Vision (ECCV) (2018)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2017)
Sharma, V., Diba, A., Neven, D., Brown, M.S., Van Gool, L., Stiefelhagen, R.: Classification-driven dynamic image enhancement. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations (ICLR) (2015)
Singh, M., Nagpal, S., Singh, R., Vatsa, M.: Dual directed capsule network for very low resolution image recognition. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2019)
Suganuma, M., Liu, X., Okatani, T.: Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: A persistent memory network for image restoration. In: Proceedings of International Conference on Computer Vision (ICCV). pp. 4539–4547 (2017)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016) 5
Vidal, R.G., Banerjee, S., Grm, K., Struc, V., Scheirer, W.J.: Ug\(^{2}\): a video benchmark for assessing the impact of image restoration and enhancement on automatic visual recognition. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV) (2018)
Wang, Z., Chang, S., Yang, Y., Liu, D., Huang, T.S.: Studying very low resolution recognition using deep networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Welinder, P., et al.: Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology (2010)
Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred target tracking by blur-driven tracker. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2011)
Wu, Z., Suresh, K., Narayanan, P., Xu, H., Kwon, H., Wang, Z.: Delving into robust object detection from unmanned aerial vehicles: a deep nuisance disentanglement approach. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2019)
Yasarla, R., Patel, V.M.: Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Yu, K., Dong, C., Lin, L., Change Loy, C.: Crafting a toolchain for image restoration by deep reinforcement learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Zendel, O., Honauer, K., Murschitz, M., Steininger, D., Fernandez Dominguez, G.: Wilddash - creating hazard-aware benchmarks. In: Proceedings of European Conference on Computer Vision (ECCV) (2018)
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. (TIP) 26(7), 3142–3155 (2017)
Acknowledgement
This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1801-05.
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Son, T., Kang, J., Kim, N., Cho, S., Kwak, S. (2020). URIE: Universal Image Enhancement for Visual Recognition in the Wild. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_43
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