![]() In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. Hardie, R.C.: High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system. Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: Deep joint demosaicking and denoising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15(1), 141–159 (2006)įoi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical poissonian-gaussian noise modeling and fitting for single-image raw-data. IEEE (2007)ĭeudon, M., et al.: HighRes-net: recursive fusion for multi-frame super-resolution of satellite imagery (2020)įarsiu, S., Elad, M., Milanfar, P.: Multiframe demosaicing and super-resolution of color images. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. Forum 34, 95–104 (2015)ĭai, S., Han, M., Xu, W., Wu, Y., Gong, Y.: Soft edge smoothness prior for alpha channel super resolution. 1251–1258 (2017)ĭai, D., Timofte, R., Van Gool, L.: Jointly optimized regressors for image super-resolution. 3185–3194 (2019)Ĭhollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. IEEE (2004)Ĭhen, C., Chen, Q., Do, M.N., Koltun, V.: Seeing motion in the dark. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. IEEE (2012)Ĭhang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. ![]() 9209–9218 (2021)īurger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 613–626 (2021)īhat, G., Danelljan, M., Van Gool, L., Timofte, R.: Deep burst super-resolution. 51, 144–154 (2018)īevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)īhat, G., Danelljan, M., Timofte, R.: NTIRE 2021 challenge on burst super-resolution: methods and results. 1692–1700 (2018)Īnaya, J., Barbu, A.: Renoir-a dataset for real low-light image noise reduction. KeywordsĪbdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. Furthermore, our approach takes the 5th place in synthetic track of the NTIRE 2022 Burst Super-Resolution Challenge. Experimental results demonstrate that our method over the existing state-of-the-art in both synthetic datasets and real datasets. In addition, we introduce a new pipeline to compensate for lost information. Also, we propose a Reconstruction Network to enhance spatial feature representation and eliminate the influence of spatial noise. ![]() We adopt a Denoising Network to further improve the performance of noise-free SR images. In this paper, we propose a new framework named A RAW Burst Super-Resolution Method with Enhanced Denoising (EDRBSR), which solves the BurstSR problem by jointly denoising structure and reconstruction enhancement structure. However, the existing networks rarely pay attention to the enhanced denoising problem in raw domain and they are not sufficient to restore complex texture relationships between frames. Deep learning-based burst super-resolution (SR) approaches are extensively studied in recent years, prevailing in the synthetic datasets and the real datasets. ![]()
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