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Microscopic analysis of neuronal cell morphology is required in many studies in neurobiology. The development of computational methods for this purpose is an ongoing challenge and includes solving some of the fundamental computer vision problems such as detecting and grouping sometimes very noisy line-like image structures. Advancements in the field are impeded by the complexity and immense diversity...
Visual object counting (VOC) is important in many real-world applications. Our previous work approximated sparsity-constrain example-based VOC (ASE-VOC) works well with insufficient training data. It assumes that image patches share the similar local geometry with counterpart density maps, and then the density map of the image patch can be estimated by preserving such geometry. However. ASE-VOC has...
In this paper, we propose a new method for the recovery of a sparse signal from few linear measurements using a reference signal as side information. Modeling the signal coefficients with a double Laplace mixture model, and assuming that the distribution of the components of the prior information differs slightly from the unknown signal, the problem is formulated as a weighted ℓ1 minimization problem...
Color images taken in low light scenes are deteriorated with noise and motion blur. The simultaneous reduction of noise and motion blur from the low-light color images is difficult because the imposed noise hinders accurate motion blur kernel estimation. To overcome this problem, we build a novel imaging system using a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) images...
In this paper, we propose new gradient-based methods for image reconstruction from partial Fourier measurements, which are commonly used in magnetic resonance imaging (MRI) or synthetic aperture radar. Compared to classical gradient recovery methods, a key improvement is obtained by formulating the gradient recovery problem as a compressed sensing problem with the additional constraint that the curl...
This paper presents an exemplar-based image completion via a new quality measure based on phaseless texture features. The proposed method derives a new quality measure obtained by monitoring errors caused in power spectra, i.e., errors of phaseless texture features, converged through phase retrieval. Even if a target patch includes missing pixels, this measure enables selection of the best matched...
We address sparse signal, i.e. image recovery in a Bayesian estimation framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the popular spike and slab prior which is considered the gold standard in the statistics literature. The optimization problem resulting from this model has broad applicability in recovery, regression and classification...
Estimation of people density in intensely dense crowded scenes is very crucial due to perspective difference, few pixels per target, clutter and complex backgrounds etc. Most of the existing work is unable to handle the crowds of hundreds or thousands. At this level of density, one feature is not enough to estimate the total density of an image. We propose a hybrid model which relies on multiple source...
Microwave ultra-wide band (UWB) radars with higher range resolution and penetration ability for low lossy medium are promising as non-destructive testing for aging transportation infrastructure or non-invasive internal inspection for human body such as cancer or brain stroke detection. While there are many studies for reconstructing complex permittivity for object based on the solution of domain integral...
In this paper, a new thresholding algorithm that can distinguish between significant and non-significant coefficient at each detail subbands using standard deviation-based wavelet coefficients threshold estimation is proposed. The proposed algorithm start with calculating the threshold value by using the proposed threshold value estimator at wavelet detail subbands (Diagonal, Vertical and Horizontal...
Low Light Level Images (LLLIs) are captured with exceptionally low brightness and low contrast, and cannot be enhanced satisfactorily with ordinary methods. In this paper, we propose a LLLI enhancement method using coupled dictionary learning. During the training stage, a pair of dictionaries and a linear mapping function are learned simultaneously. The dictionary pair aims to describe the raw LLLIs...
Structure from motion (SfM) and self-calibration from images of unknown radial distortions could fail under some critical configurations and produce distorted reconstruction results. In this paper, we propose an effective approach to optimize the estimation of radial distortion coefficient by taking full advantage of GPS information, which allows for more accurate SfM results. A feedback function...
How to average translations is the single most difficult task in global structure-from-motion (SfM) to fully tap its potentials in terms of reconstruction efficiency and accuracy since usually only noisy translation directions can be factored out from essential matrices due to the inevitable matching outliers. To tackle this problem, this work proposes a two-step strategy. Firstly, a “2-point method”...
This paper presents a fast deblurring algorithm to remove camera motion blur from a single photograph using built-in gyroscopes and strong edge prediction. An inaccurate blur kernel or point spread function (PSF) usually leads to an unsatisfying restored result. Hence, we propose a robust three-phase method for accurate PSF estimation. In the first stage, we utilize the embedded gyroscopes to compute...
This paper discusses the refinement of sparse and noisy depth-maps to improve stereo measurements. Our method functions as a post-filter for stereo measurements, to remove outliers and interpolate the depths of invalid pixels. Per-pixel plane fitting is employed to estimate the normals of an object's surface in a depth-map. These normals provide information regarding the interpolation of depth and...
Background Estimation in video consists in extracting a foreground-free image from a set of training frames. In this paper, we overview a temporal-spatial block-level approach for background estimation in video and present their results in the SBMnet dataset. First, the employed approach uses a Temporal Analysis module to obtain a compact representation of the training data that is later clustered...
Multi-frame super resolution has been well studied in recent years, but blur kernel is always assumed to be known in video super resolution problem. Most blind deconvolution algorithm can both estimate the blur kernel and the sharp image. In this paper, we originally adopt a fast single image blind deconvolution algorithm in video super resolution to estimate high-resolution image and blur kernel...
Although visual tracking have been greatly improved in the last decade, there are still many challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting, are often ignored. Under the framework of particle filtering, this paper uses the incremental principal component analysis subspace method to learn an orthogonal subspace, then gets the linear representation...
We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts. In contrast, we propose an image recovery method free of post-processing, where we utilize a lapped transform (LT) for the sparse representation of the image in order to reduce...
Image blurring attenuate crucial textures and thus always result in a pretty dismal visual experience. Unfortunately, image blurring is difficult to avoid during the image acquisition, hence, lots of recent research focus on how to preserve subtle textures while suppress visual artifacts during image deblurring. Among all of the existing image deblurring methods, image priors, such as non-local priors...
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