# 1 bit compressive sensing

### 1104.3160 Robust 1-Bit Compressive Sensing via Binary

· Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors. The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite

### 1-Bit Compressed Sensing SpringerLink

· Robust 1-bit compressive sensing using adaptive outlier pursuit. IEEE Transactions on Signal Processing 60(7) 3868–3875 July 2012. MathSciNet CrossRef Google Scholar. A. Zymnis S. Boyd and E. Candès. Compressed sensing with quantized measurements.

### 1-Bit Compressive SensingBoufounos

· 1-Bit Compressive Sensing Petros T. Boufounos Richard G. Baraniuk Rice University Electrical and Computer Engineering 6100 Main St. MS 380 Houston TX 77005 Abstract—Compressive sensing is a new signal acquisition tech-nology with the potential to reduce the number of measurements required to acquire signals that are sparse or compressible

### Robust 1-Bit Compressive Sensing via Binary Stable

· Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors Laurent Jacquesy Jason N. Laska z Petros T. Boufounos x and Richard G. Baraniuk z April 15 2011 Abstract The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital con-

### 1104.3160 Robust 1-Bit Compressive Sensing via Binary

· Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors. The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite

### Robust mixed one-bit compressive sensingScienceDirect

· Mixed one-bit compressive sensing In this paper we are dealing with a sensing system (1) of which a part of the measurements are saturated. Since there are both analog and one-bit measurements we propose a new method from both regular CS and 1bit-CS and thus call the new method as mixed one-bit compressive sensing (M1bit-CS).

Cited by 2### 1701.00694 Mixed one-bit compressive sensing with

· Aiming at overexposure correction for computed tomography (CT) reconstruction we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing which deals with only sign observations.

### 1-bit compressive sensing with an improved algorithm based

· In general there are two tasks in 1-bit compressive sensing (1) support recovery which aims to recover supports of unknown signals (2) approximate signal vector recovery which recovers the a unit vector x such that ∥ x − x ∥ 2 is small. Thus in the following simulations the two tasks will be discussed respectively.

Cited by 13### 1-bit Compressive Sensing_xp_fangfei

Translate this page· 1-bit Compressive Sensing_xp_fangfei-CSDN. xp_fangfei. CSDN CSDN. 5 . 16. . 57 . . 32 .

### 1-bit Compressive Sensing via AMP with Built-in Parameter

1-bit Compressive Sensing (CS) tries to recover a sparse signal from quantized 1-bit measurements. 1-bit CS can be straightforwardly extended to multi-bit CS that tries to recover a sparse signal from quantized multi-bit measurements. We propose to solve the two problems using the proposed AMP with built-in parameter estimation (AMP-PE) 1 .

### One-bit compressive sensing of dictionary-sparse signals

Abstract. One-bit compressive sensing has extended the scope of sparse recovery by showing that sparse signals can be accurately reconstructed even when their linear measurements are subject to the extreme quantization scenario of binary samples—only the

### One-Bit Compressive Sensing of Dictionary-Sparse Signals

· on the sensing matrix (satis ed by Gaussian matrices) these algorithms can e ciently recover analysis-dictionary-sparse signals in the one-bit model. Key words and phrases compressive sensing quantization one-bit compressive sensing tight frames convex optimization thresholding. 1 Introduction The basic insight of compressive sensing is

### Robust recovery in 1-bit compressive sensing via ℓq

· The 1-bit compressive sensing which is the extreme case of quantization has drawn much attention because of its low cost in hardware implementation and storage . The 1-bit compressive sensing has been successfully applied to many fields such as signal processing distributed networks and machine learning .

### 1104.3160 Robust 1-Bit Compressive Sensing via Binary

· Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors. The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite

### One-bit compressive sensing of dictionary-sparse signals

Abstract. One-bit compressive sensing has extended the scope of sparse recovery by showing that sparse signals can be accurately reconstructed even when their linear measurements are subject to the extreme quantization scenario of binary samples—only the

### 1701.00694 Mixed one-bit compressive sensing with

· Aiming at overexposure correction for computed tomography (CT) reconstruction we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing which deals with only sign observations.

### 1-bit Compressive Sensing_xp_fangfei

Translate this page· 1-bit Compressive Sensing_xp_fangfei-CSDN. xp_fangfei. CSDN CSDN. 5 . 16. . 57 . . 32 .

### Robust recovery in 1-bit compressive sensing via ℓq

· The 1-bit compressive sensing which is the extreme case of quantization has drawn much attention because of its low cost in hardware implementation and storage . The 1-bit compressive sensing has been successfully applied to many fields such as signal processing distributed networks and machine learning .

### 1-bit Compressive Sensing via AMP with Built-in Parameter

1-bit Compressive Sensing (CS) tries to recover a sparse signal from quantized 1-bit measurements. 1-bit CS can be straightforwardly extended to multi-bit CS that tries to recover a sparse signal from quantized multi-bit measurements. We propose to solve the two problems using the proposed AMP with built-in parameter estimation (AMP-PE) 1 .

### 1-Bit compressive sensing IEEE Conference Publication

· 1-Bit compressive sensing. Abstract Compressive sensing is a new signal acquisition technology with the potential to reduce the number of measurements required to acquire signals that are sparse or compressible in some basis. Rather than uniformly sampling the signal compressive sensing computes inner products with a randomized dictionary of test

Cited by 719### 1-Bit Compressed Sensing SpringerLink

· Robust 1-bit compressive sensing using adaptive outlier pursuit. IEEE Transactions on Signal Processing 60(7) 3868–3875 July 2012. MathSciNet CrossRef Google Scholar. A. Zymnis S. Boyd and E. Candès. Compressed sensing with quantized measurements.

### Efficient Algorithms for Robust One-bit Compressive

· 3. Efﬁcient Algorithms for One-bit Compressive Sensing (CS) We ﬁrst introduce notations and assumptions of one-bit compressive sensing. We then present both passive and adaptive algorithms for one-bit compressive sensing fol-lowed by their theoretical guarantees. 3.1. Preliminary Let x∗ ∈ Rn be a sparse or compressible vector to be

### One-Bit Compressive Sensing of Dictionary-Sparse Signals

· on the sensing matrix (satis ed by Gaussian matrices) these algorithms can e ciently recover analysis-dictionary-sparse signals in the one-bit model. Key words and phrases compressive sensing quantization one-bit compressive sensing tight frames convex optimization thresholding. 1 Introduction The basic insight of compressive sensing is

### Robust 1-Bit Compressive Sensing via Binary Stable

· The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits moreover there is an inverse relationship between the achievable sampling rate and the bit depth. In this paper

### 1-Bit compressive sensing Reformulation and RRSP-based

· Recently the 1-bit compressive sensing (1-bit CS) has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption (that has been overlooked in the literature) should be made for

### 1-bit Compressive Sensing via AMP with Built-in Parameter

1-bit Compressive Sensing (CS) tries to recover a sparse signal from quantized 1-bit measurements. 1-bit CS can be straightforwardly extended to multi-bit CS that tries to recover a sparse signal from quantized multi-bit measurements. We propose to solve the two problems using the proposed AMP with built-in parameter estimation (AMP-PE) 1 .

### 1-bit Compressive Sensing Petros T. Boufounos

1-bit Compressive Sensing. 1 -bit Compressive Sensing studies how Compressive Sensing interacts with a particularly interesting quantizer. Specifically it examines the most severe form of scalar quantization which preserves only the sign of each measurement as one bit of information. I first introduced this line of work in 1 and followed

### 1-Bit Compressed Sensing SpringerLink

· Robust 1-bit compressive sensing using adaptive outlier pursuit. IEEE Transactions on Signal Processing 60(7) 3868–3875 July 2012. MathSciNet CrossRef Google Scholar. A. Zymnis S. Boyd and E. Candès. Compressed sensing with quantized measurements.

### Review on One-Bit Compressive Sensing and its Biomedical

· Abstract As a specific class of compressive sensing one-bit compressive sensing can use sign measurements (one-bit information) to recover sparse signals. One-bit information can be sampled in a very high rate with a relatively low cost. The progress of 1bit-CS including models algorithms and applications is reviewed in this paper.