Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts suitable parameters and the decoder is fixed to a physically meaningful model function, such that we can train the encoding network in an unsupervised way. We illustrate numerically that the resulting network is more than 140 times faster than classical optimization techniques while making predictions with only slightly higher objective values. Using such predictions as starting points of local optimization techniques allows us to converge to better local minima about twice as fast as optimization without the network-based initialization.
Finally, a recent work has exploited deep learning techniques in Terahertz imaging in @cite_20 , but the considered application of super-resolving the THz amplitude image @math by training a convolutional neural network on synthetically blurred images is not directly related to our proposed approach.
@cite_20
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We propose an effective and robust method for terahertz (THz) image super-resolution based on a deep convolutional neural network (CNN). A deep CNN model is designed. It learns an end-to-end mapping between the low- and high-resolution images. Blur kernels with multiple width and noise with multiple levels are taken into the training set so that the network can handle THz images very well. Quantitative comparison of the proposed method and other super-resolution methods on the synthetic THz images indicates that the proposed method performs better than other methods in accuracy and visual improvements. Experimental results on real THz images show that the proposed method significantly improves the quality of THz images with increased resolution and decreased noise, which proves the practicability and exactitude of the proposed method. The problem of enhancing the resolution of a single low-resolution image has been popularly addressed by recent deep learning techniques. However, many deep learning approaches still fail to deal with extreme super-resolution scenarios because of the instability of training. In this paper, we address this issue by adapting a progressive learning scheme to the deep convolutional neural network. In detail, the overall training proceeds in multiple stages so that the model gradually increases the output image resolution. In our experiments, we show that this property yields a large performance gain compared to the non-progressive learning methods. In recent years, the widespread use of deep neural networks (DNNs) has facilitated great improvements in performance for computer vision tasks like image classification and object recognition. In most realistic computer vision applications, an input image undergoes some form of image distortion such as blur and additive noise during image acquisition or transmission. Deep networks trained on pristine images perform poorly when tested on such distortions. In this paper, we evaluate the effect of image distortions like Gaussian blur and additive noise on the activations of pre-trained convolutional filters. We propose a metric to identify the most noise susceptible convolutional filters and rank them in order of the highest gain in classification accuracy upon correction. In our proposed approach called DeepCorrect , we apply small stacks of convolutional layers with residual connections at the output of these ranked filters and train them to correct the worst distortion affected filter activations, while leaving the rest of the pre-trained filter outputs in the network unchanged. Performance results show that applying DeepCorrect models for common vision tasks like image classification (ImageNet), object recognition (Caltech-101, Caltech-256), and scene classification (SUN-397), significantly improves the robustness of DNNs against distorted images and outperforms other alternative approaches. Deep learning has recently been introduced to the field of low-level computer vision and image processing. Promising results have been obtained in a number of tasks including super-resolution, inpainting, deconvolution, filtering, etc. However, previously adopted neural network approaches such as convolutional neural networks and sparse auto-encoders are inherently with translation invariant operators. We found this property prevents the deep learning approaches from outperforming the state-of-the-art if the task itself requires translation variant interpolation (TVI). In this paper, we draw on Shepard interpolation and design Shepard Convolutional Neural Networks (ShCNN) which efficiently realizes end-to-end trainable TVI operators in the network. We show that by adding only a few feature maps in the new Shepard layers, the network is able to achieve stronger results than a much deeper architecture. Superior performance on both image in-painting and super-resolution is obtained where our system outperforms previous ones while keeping the running time competitive.