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Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning-based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics-based learned reconstruction approach compared to commonly used approaches in terms of both runtime and image quality at highly compressive settings. Our source codes and dataset are made available at https://github.com/METU-SPACE-Lab/Efficient-Learned-3D-Near-Field-MIMO-Imaging upon publication to advance research in this field. • 3D image reconstruction methods are developed for near-field MIMO radar imaging using deep learning-based direct inversion and 3D CNNs. • A synthesizer to generate 3D scenes with extended targets is developed to obtain large data for training the neural networks. • Resolution analysis is performed for compressive imaging settings with sparse arrays. • Performance is comprehensively evaluated with simulated and experimental data, using both magnitude-only and complex-valued processing. • The results show the effectiveness of the developed physics-based learned reconstruction method in terms of image quality and runtime. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones. [ABSTRACT FROM AUTHOR]

Digital images are playing a progressively important role in almost all the fields such as computer science, medicine, communications, transmission, security, surveillance, and many more. Digital images are susceptible to a number of distortions due to faulty imaging instruments, transmission channels, atmospheric and environmental conditions, etc. resulting in degraded images. Degradation can be of different types such as noise, backscattering, low saturation, low contrast, tilt, spectral absorption, blurring, etc. The degradation reduces digital images' effectiveness and therefore needs to be restored. In this paper, we present an extensive review of image restoration tasks. It addresses problems like image deblurring, denoising, dehazing and super-resolution. Image restoration is fundamentally an image processing problem, but deep learning techniques, based mainly on convolutional neural networks have received a lot of attention in almost all areas of computer science. Along with deep learning, other machine learning methods have also been tried for restoring digital images. In this review, we have therefore categorized digital image restoration techniques as either image processing-based, machine learning-based or deep learning-based. For each category, a variety of approaches presented in recent years have been reviewed. This review also includes a summary of the data sets used for image restoration along with a baseline reference that can be used by future researchers to compare and improve their results. We also suggest some interesting research directions for future work in this area. [ABSTRACT FROM AUTHOR]