Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising
Stacked sparse denoising autoencoders (SSDAs) have recently been shown to be successful at removing noise from corrupted images. However, like most denoising techniques, the SSDA is not robust to variation in noise types beyond what it has seen during training. To address this limitation, we present the adaptive multi-column stacked sparse denoising autoencoder (AMC-SSDA), a novel technique of combining multiple SSDAs by (1) computing optimal column weights via solving a nonlinear optimization program and (2) training a separate network to predict the optimal weights. We eliminate the need to determine the type of noise, let alone its statistics, at test time and even show that the system can be robust to noise not seen in the training set. We show that state-of-the-art denoising performance can be achieved with a single system on a variety of different noise types. Additionally, we demonstrate the efficacy of AMC-SSDA as a preprocessing (denoising) algorithm by achieving strong classification performance on corrupted MNIST digits.
This paper uses deep neural networks to denoise 
      images. The network uses several independent columns trained on different 
      types of noise. Furthermore, a weight prediction module is trained to 
      decide how to merge these columns to predict the clean image from the 
      noisy image. The idea is to have only one network for all types of noises 
      and noise strengths. Noise is not always Gaussian, and it may 
      sometimes be beneficial to have a single method that can adapt to 
      different noise types. Engineered methods like BM3D are hard to modify to 
      arbitrary noise types, learned methods like Burger et al. have to be 
      retrained (or also the missing citation Jancsary et al., "Loss-Speci fic 
      Training of Non-Parametric Image Restoration Models"). This method 
      solves it by training different models on different noises and then 
      combining the results. The method is shown to be successful in denoising 
      different types of noises after a one-time training. Denoising also works 
      on "unseen" noise that wasn't trained on in any of its submodels. 
      However, it is inferior to just training on a single noise type for 
      that specific noise type: SSDA outperforms MC-SSDA on the CT images on 
      Gaussian noise type 2 and 3, which have similar PSNR to the training PSNR 
      of SSDA. The same holds true for the MNIST comparison. Furthermore, the 
      SSDA is also able to denoise unseen noise types to some extent, in one 
      case even better. [The authors were able to address this issue during the 
      rebuttal by training a new model with additional columns.] Also, it's 
      not compared to standard denoising methods on natural images, which I 
      would expect. On digital photos the noise usually is a mixture of Gaussian 
      and Poisson noise, which the method should be able to handle. I wonder 
      what the runtime of the method is (training/testing)? Also, how does the 
      performance change if the number of autoencoders is varied? The 
      paper is well structured and easy to understand. Very beneficial is that 
      the code would be published after publication.