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We propose a high accuracy algorithm for compressed sensing magnetic resonance imaging (CS-MRI) using a convex optimization technique. Lustig et al. proposed CS-MRI technique based on the minimization of a cost function defined by the sum of the data fidelity term, the 11-norm of sparsifying transform coefficients, and a total variation (TV). This function is not differentiable because of both l1-norm...
Fourier encoding is commonly used in modern magnetic resonance imaging (MRI) scanners. However, the partial Fourier matrix is weakly incoherent with the wavelet transform matrix. It implies that the subsampled $k$-space data set may not be a satisfactory option for compressive sensing (CS) based image reconstruction. On the contrary, noiselet matrix has proven to be perfectly incoherent with Haar...
At low bit-rates, the conventional image coding standards, e.g., JPEG and JPEG 2000, do not have good compression performance due to the insufficiency of coding bits. A common solution to this problem is downsampling before encoding and reconstruction after decoding. Inspired by the wavelet domain downsampling-based compression scheme, we establish an enhanced low bit-rates coding framework by making...
In this paper, we propose a method to separate diffuse and specular images from a single image based on sparse representation. The proposed method relax the recent sparse-based technique for removing specular highlights that require multiple input images. Instead, we use only a single input image and then generate multiple specular-free images from this image. The specular removal problem is formulated...
The coefficient of high resolution (HR) block and low resolution (LR) block is assumed to be equal in selecting the corresponding atoms of HR dictionary in previous super-resolution (SR) reconstruction algorithms, which may cause error matching and decrease the accuracy of HR coefficient estimation. A learning method of structural dictionary and mapping relation (LCDMR) is combined to compensate this...
This paper proposes improvements to existing cold boot attack algorithms which greatly reduce the number of correlate tests required at the expense of some memory in discrete logarithm based cryptosystems. In practical key recovery settings, the excess memory incurred is shown to be insignificant when the variable parameter in the algorithms is optimized. The results show that improvements of up to...
High Dynamic Range (HDR) videos are commonly compressed using existing Low Dynamic Range (LDR) coding standards. This necessarily implies a Tone Mapping (TM) stage which aims at reducing the input video range while preserving the most significant scene details. In most cases, a residual sequence is encoded and used at the decoder side to recover the HDR content. The challenge is to efficiently encode...
H.265/High Efficiency Video Coding (HEVC) is the latest next generation video compression standard posterior to H.264/AVC. However, despite its superior coding efficiency to the previous video coding standards, the complexity to implement it is an obstacle to overcome. Especially, combining the separate encoder and decoder has a disadvantage on the aspect of the size and power consumption. To solve...
The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of...
Addressing transmission errors in underwater acoustic channels is a key challenge for the real-time video communication between an autonomous underwater vehicle and a surface station. In this paper, we propose an error-resilient video compression technique based on hybrid multiple descriptions and redundant pictures to overcome impact of packet loss in underwater acoustic transmission. Video sequences...
In structured light 3D depth measurement, capturing 3D point cloud from the object surface with low reflectance is difficult. Furthermore, it is hard to distinguish between the shade and the object surface with low reflectance, such that many outliers are generated from the shade where no 3D point cloud is supposed to be captured. This paper presents a method to distinguish the shade from the object...
Our challenge is the design of a “universal” bit-efficient image compression approach. The prime goal is to allow reconstruction of images with high quality. In addition, we attempt to design the coder and decoder “universal”, such that MPEG-7-like low-and mid-level descriptors are an integral part of the coded representation. To this end, we introduce a sparse Mixture-of-Experts regression approach...
In this paper, we present a novel classification model which combines the convolutional sparse coding framework with the classification strategy. In the training phase, the proposed model trained a convolutional filter bank by all images of each class. In the test phase, the label of test image is determined by all convolutional filter banks. Compared with canonical sparse representation and dictionary...
In this paper, we propose a novel single image super-resolution (SR) method based on low-rank sparse representation with self-similarity learning. Sparse representation is known as a promising method for SR. However, the sparse codes for low resolution (LR) patches gained by conventional method are not faithful to those for the original high resolution (HR) ones. To overcome this defect, we explore...
Recently, an annihilating filter based low-rank Hankel matrix approach (ALOHA) was proposed as a general framework for sparsity-driven k-space interpolation method for compressed sensing MRI (CS-MRI). The principle of ALOHA framework is based on the fundamental duality between the transform domain sparsity in the primary space and the low-rankness of weighted Hankel matrix in Fourier domain, which...
Adaptive sparse representation has been heavily exploited in signal processing and computer vision. Recently, sparsifying transform learning received interest for its cheap computation and optimal updates in the alternating algorithms. In this work, we develop a methodology for learning a Flipping and Rotation Invariant Sparsifying Transform, dubbed FRIST, to better represent natural images that contain...
Fisher discrimination dictionary sparse learning (FDDL) has led to interesting image recognition results where the Fisher discrimination criterion is subject to the coding coefficients. But Fisher discrimination criterion has the limitations of data distribution assumptions and does not consider the local manifold structure of the coding coefficients. In this paper, we will introduce a novel Fisher...
With the development of surveillance cameras, person re-identification has gained much interest, however re-identifying people across cameras remains a challenging problem which not only requires a good feature description but also a reliable matching scheme. Our method can be applied with any feature and focuses on the second requirement. We propose a robust bidirectional sparse coding method that...
Ultrasound computed tomography (USCT) is a promising medical imaging modality that offers several novel tissue contrasts and holds great potential for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique was combined...
For lower storage costs, storage systems are increasingly transitioning to the use of erasure codes instead of replication. However, the increase in the amount of data to be read and transferred during recovery for an erasure-coded system results in the problem of high degraded read latency. We design a new parallel degraded read method, Collective Reconstruction Read, which aims to overcome the problem...
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