1. 毕业设计（论文）的内容和要求
图像反问题是传感、成像和计算机视觉等领域的重要研究对象。
随着技术的发展，处理反问题的手段从迭代的基于物理模型的方法[1]-[3]逐渐步入到基于数据训练的深度学习方法[4]-[7]，并在图像去噪、超分辨、图像修复和压缩感知等领域得到了巨大进步。
目前针对图像重建和传感问题的最优方法大都来源于深度学习，通过对信号表达、参数学习、正则化甚至整个逆函数的学习为逆问题的处理提供了更有效的解决方案。
2. 参考文献
[1]. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. 16 (8) (2007) 20802095.[2]. J. Mairal, F. Bach, J. Ponce, G. Sapiro, A. Zisserman, Non-local sparse models for image restoration, in Proc. IEEE Int. Conf. Comput. Vis., Tokyo, Japan, 2009, pp. 22722279.[3]. S. Gu, Q. Xie, D. Meng, W. Zuo, X. Feng, L. Zhang, Weighted nuclear norm minimization and its applications to low level vision, Int. J. Comput. Vision. 121 (2) (2017) 183-208.[4]. K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 31423155, July 2017.[5]. W. Bae, J. J. Yoo, and J. C. Ye, Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification, CoRR, vol. abs/1611.06345, 2016. [Online]. Available: http://arxiv. org/abs/1611.06345[6]. Kai Zhang, Wangmeng Zuo, and Lei Zhang. FFDNet: Toward a fast and flexible solution for CNN based image denoising. IEEE T. Image Process., 27(9):46084622, 2018[7]. Pengju Liu ; Hongzhi Zhang ; Kai Zhang ; Liang Lin ; Wangmeng Zuo Multi-level Wavelet Convolutional Neural Networks. arXiv:1907.03128[8]. J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila. Noise2Noise: Learning image restoration without clean data. In ICML, pages 29652974, 2018.[9]. Joshua Batson and Loic Royer. Noise2Self: Blind denoising by self-supervision. In ICML, volume 97, pages 524533.PMLR, 2019[10]. Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2Void-learning denoising from single noisy images. In CVPR, 2019.[11]. Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. In NeurIPS, 2019.[12]. D. Ulyanov, A. Vedaldi, and V. Lempitsky. Deep image prior. In Proc. IEEE Conf. Comp. Vision and Pattern Recog. (CVPR), pages 94469454, 2018 [13]. C. Metzler, A. Mousavi, R. Heckel, R. Baraniuk. Unsupervised Learning with Stein's Unbiased Risk Estimator. International Biomedical and Astronomical Signal Processing (BASP) Frontiers workshop 2019. Best contribution award.
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