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A configurable neuro-inspired inference processor is designed as an array of neurons each operating in an independent clock domain. The processor implements a recurrent network using efficient sparse convolutions with zero-patch skipping for feedforward operations, and sparse spike-driven reconstruction for feedback operations. A globally asynchronous locally synchronous structure enables scalable...
Blob detection and image denoising are fundamental, and sometimes related, tasks in computer vision. In this paper, we propose a blob reconstruction method using scale-invariant normalized unilateral second order Gaussian kernels. Unlike other blob detection methods, our method suppresses non-blob structures while also identifying blob parameters, i.e., position, prominence and scale, thereby facilitating...
The Computed Tomography (CT) is a imaging method based on X-rays to obtain cross-sectional images from an object. It is a widely used method in several areas, such as medicine, archeology or material sciences. Tomographic reconstruction techniques, use the projections of images from multiple directions. There are several algorithms for this purpose but can be classified according to their reconstruction...
In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated...
We present an algorithm to directly restore a clear highresolution image from a blurry low-resolution input. This problem is highly ill-posed and the basic assumptions for existing super-resolution methods (requiring clear input) and deblurring methods (requiring high-resolution input) no longer hold. We focus on face and text images and adopt a generative adversarial network (GAN) to learn a category-specific...
This paper presents our study on image reconstruction algorithms for THz near field scanning systems. Based on the principle of Physical Optics (PO) algorithm, we have proposed and investigated a novel transposed convolution image reconstruction algorithm (TC), in comparison with the back propagation algorithm (BP) in simulation and experiment.
Radiofrequency and microwave ablation procedures are commonly used for treating hepatocellular carcinoma in patients that are not candidates for surgical resection. Ablation is a minimally invasive procedure which involves insertion of an ablation needle (electrode) into the tumor which produces localized heating to kill the surrounding cancerous cells. Ultrasound can be a valuable tool for real-time...
Maximum Entropy (MaxEnt) and Compressive Sensing (CS) are two paradigms that allow good image reconstruction from a low number of measurements. MaxEnt is based on the maximization of entropy while CS uses the minimization of l1 norm of image sparse representation. In this paper, MaxEnt and CS are tested in conditions simulating the acquisition by Single Pixel Camera. The set of measurements is obtained...
This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional...
This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural...
Principal component analysis (PCA) is one of the most versatile tools for unsupervised learning with applications ranging from dimensionality reduction to exploratory data analysis and visualization. While much effort has been devoted to encouraging meaningful representations through regularization (e.g. non-negativity or sparsity), underlying linearity assumptions can limit their effectiveness. To...
In this paper, we take advantage of the clear texture structure of the epipolar plane image (EPI) in the light field data and model the problem of light field reconstruction from a sparse set of views as a CNN-based angular detail restoration on EPI. We indicate that one of the main challenges in sparsely sampled light field reconstruction is the information asymmetry between the spatial and angular...
Thin structures such as fence, grass and vessels are common in photography and scientific imaging. They exhibit complex 3D structures with sharp depth variations/discontinuities and mutual occlusions. In this paper, we develop a method to estimate the occlusion matte and depths of thin structures from a focal image stack, which is obtained either by varying the focus/aperture of the lens or computed...
This paper proposes a target detector based on kernel sparse and spatial constraint for hyperspectral imagery (HSI). Due to the nonlinear and structural features of HSI data, sparse representation and spatial constraint are taken into consideration. Firstly, we construct a dictionary to represent the target pixels within a small neighborhood by a linear combination of samples. Then, these targets...
In this paper, we propose a top-down building reconstruction technique based on layover modelling and MCMC method. Through representing the layover with parameterized geometrical models, the problem is converted into an optimization problem under the Bayesian scheme. The energy function consists of two parts: region part and edge part. In order to obtain global optima, simulated annealing algorithm...
In this work we establish sampling theorems for functions in Besov spaces on the d-dimensional sphere Sd in the spirit of their recent counterparts established by Jaming-Malinnikova in [7]. The main tool is the needlet decomposition given by Narcowich et al. in [10].
Video super-resolution (SR) is an inverse problem, which has gained much attention in these years. One of the core issues is how to better suppress noise and better preserve the edge. Multi-non-local regularization (MNLR) algorithm is efficient to reduce noise by utilizing the useful information from the correlated frames, but it might also cause some loss of high frequency at the same time. Multi-scale...
Image processing tasks has found a new dimension with the improvement of learning feature representation from images using deep networks. Most of the research works are conducted over pre-possessed image data in the lab. But, these methods fail in the real world scenario as most of the time the image required to classify is subject to noise and other disfigurement. For the last three decades, many...
We propose a stable and fast reconstruction technique for parallel-beam (PB) tomographic X-Ray imaging, relying on the discrete pseudo-polar (PP) Radon and PP Fourier transforms. Our main contribution is a resampling method, based on modern sampling theory, that transforms the PB measurements to a PP grid. The resampling process is both fast and accurate, and in addition, simultaneously denoises the...
Spatio-temporal atlas is a useful tool in imaging studies of neurodevelopment, which characterizes the growth of brain, and allows the monitoring of its development. The imaging of preterm and term born infants provides opportunities to develop a series of spatio-temporal atlases that track the changes during the particular period of neurodevelopment between. The aim of this paper is to develop a...
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