去噪算法整理

去噪算法整理

一、傳統去噪方法 ：

1. non-local means
2. BM3D：Dabov, K., et al. (2007). “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering.” IEEE Transactions on Image Processing 16(8): 2080-2095.
3. Xu J, Zhang L, Zhang D. A trilateral weighted sparse coding scheme for real-world image denoising[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 20-36.：對實際噪聲數據建模，採用稀疏表達去噪
4. Nam S, Hwang Y, Matsushita Y, et al. A holistic approach to cross-channel image noise modeling and its application to image denoising[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1683-1691.：一種彩色圖像噪聲建模方法
5. Song P, Rodrigues M R D. Multimodal Image Denoising Based on Coupled Dictionary Learning[C]//2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018: 515-519：有一個無噪的guidance image，對無噪的guidance image和noisy image同時建立字典，中間有部分典元共享

二、基於深度學習的去噪方法

1. CSF：Schmidt U, Roth S. Shrinkage fields for effective image restoration[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 2774-2781.
2. TNRD：Chen Y, Yu W, Pock T. On learning optimized reaction diffusion processes for effective image restoration[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 5261-5269.
3. Red-Net： Mao X, Shen C, Yang Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C]//Advances in neural information processing systems. 2016: 2802-2810.
4. DnCNN： Zhang, K., et al. (2017). “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.” IEEE Transactions on Image Processing 26(7): 3142-3155.：基於添加高斯噪聲的合成數據集
5. Chen J, C. J., Chao H, et al. (2018). “Image Blind Denoising With Generative Adversarial Network Based Noise Modeling.” CVPR.：使用gan模擬實際噪聲的分佈，實際噪聲分佈通過平滑區域濾波得到
6. CBDNet：Guo S, Yan Z, Zhang K, et al. Toward convolutional blind denoising of real photographs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 1712-1722.：建立實際噪聲模型，通過噪聲模型生成有噪訓練數據集

一些實際噪聲數據集

1. Real-world Noisy Image Denoising: A New Benchmark.
2. Plotz T, Roth S. Benchmarking denoising algorithms with real photographs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1586-1595.