Image Super-Resolution Reconstruction Based On L1/2 Sparsity

Deng, Chengzhi; Liu, Juanjuan; Tian, Wenxi; Wang, Shengqian; Zhu, Haihong; Zhang, Shaoquan

doi:10.12928/eei.v3i3.284

Cited by 8 publications

(14 citation statements)

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“…A solution space needs to be defined, over which the model meets this criterion and still simulates any input signal. Furthermore, (12) in general is a nonlinear, nonconvex problem. The authors propose to use differential evolution [40] for solving (12), as it can search large solution spaces with no regards to the model's linearity.…”

Section: The Proposed Solutionmentioning

confidence: 99%

“…It can only be applied on certain signals and the entire framework-sampling and reconstruction-has to be tailored to each individual application. Despite this disadvantage CS has found its way into applications such as medical imaging [5][6][7][8], audio [9] and video [10][11][12] processing, vibration sensing [13,14] data gathering [15] etc.…”

Section: Introductionmentioning

confidence: 99%

“…None of these methods is truly versatile, some are more advantageous in a certain application than others. Research in this area continues, general approaches [6,14,35] as well as specific use cases [4,12] are being proposed.…”

Section: Introductionmentioning

confidence: 99%

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Compressed sensing with continuous parametric reconstruction

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Michaeli

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This work presents a novel unconventional method of signal reconstruction after compressive sensing. Instead of usual matrices, continuous models are used to describe both the sampling process and acquired signal. Reconstruction is performed by finding suitable values of model parameters in order to obtain the most probable fit. A continuous approach allows more precise modelling of physical sampling circuitry and signal reconstruction at arbitrary sampling rate. Application of this method is demonstrated using a wireless sensor network used for freshwater quality monitoring. Results show that the proposed method is more robust and offers stable performance when the samples are noisy or otherwise distorted.

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Section: The Proposed Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compressed sensing with continuous parametric reconstruction

Andráš

Michaeli

Šaliga

2021

IJECE

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“…Based on the idea of scattering model decomposition, a variety of incoherent decomposition models were proposed, such as four-component scattering model and multiplecomponent scattering model (MCSM) [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Many descriptive parameters can be extracted by coherent and incoherent decomposition methods based on polarimetric characteristics. Freeman and Durden suggested a three-component decomposition method of polarimetric data, which introduced a combination of surface, double-bounce, and volume scatterings.Based on the idea of scattering model decomposition, a variety of incoherent decomposition models were proposed, such as four-component scattering model and multiplecomponent scattering model (MCSM) [1][2][3][4][5][6][7][8].The PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The extensive analysis of the physical mechanism is difficult, and for complex scenes, the underlying physical mechanism of each pixel is hard to obtain.…”

mentioning

confidence: 99%

A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation

Wang

Xia

2016

TELKOMNIKA

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IntroductionFeature extraction and PolSAR image interpretation are of much theoretical and application significance, and recently, the extensive attention in PolSAR remote sensing has gradually arisen. PolSAR related researches have been conducted for many years, and a variety of methods have been proposed. Many of the proposed techniques are based on physical scattering mechanisms obtained from different polarimetric decomposition methods. Polarimetric target decomposition theorem expresses the average mechanism as the sum of independent elements in order to associate a physical mechanism with each resolution cell, which allows the identification and separation of scattering mechanisms in polarization signature for purposes of classification and recognition. Many descriptive parameters can be extracted by coherent and incoherent decomposition methods based on polarimetric characteristics. Freeman and Durden suggested a three-component decomposition method of polarimetric data, which introduced a combination of surface, double-bounce, and volume scatterings.Based on the idea of scattering model decomposition, a variety of incoherent decomposition models were proposed, such as four-component scattering model and multiplecomponent scattering model (MCSM) [1][2][3][4][5][6][7][8].The PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The extensive analysis of the physical mechanism is difficult, and for complex scenes, the underlying physical mechanism of each pixel is hard to obtain. Thus, the high-level image processing and learning machine are useful for the classification of the complex scenes. Sparse representation of signals based on over-complete dictionary is a kind of new signal representation theory, which substitutes over-complete redundant function system for traditional orthogonal basis functions and provides great flexibility for adaptive sparse extension of signals [9][10][11][12][13]. It aims to approximate a target signal using a linear combination of elementary signals from a large candidate set, which is called as "dictionary", with each element called as "atom". Sparse representation has therefore increasingly become recognized as providing extremely high performance for diverse applications. The sparse representation-based target classification is a kind of new theory in SAR images processing, and has been proved to be rather efficacious [14].

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Experimental analysis of non-Gaussian noise resistance on global method optical flow using bilateral in reverse confidential

Kesrarat¹,

Patanavijit²

2021

Bulletin EEI

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This paper presents the analytical of non-Gaussian noise resistance with the aid of the use of bilateral in reverse confidential with the optical flow. In particular, optical flow is the sample of the image’s motion from the consecutive images caused by the object’s movement. It is a 2-D vector where every vector is a displacement vector displaying the motion from the first image to the second. When the noise interferes with the image flow, the approximated performance on the vector in optical flow is poor. We ensure greater appropriate noise resistance by applying bilateral in reverse confidential in optical flow in the experiment by concerning the error vector magnitude (EVM). Many noise resistance models of the global method optical flow are using for comparison in our experiment. And many sequenced image data sets where they are interfered with by several types of non-Gaussian noise are used for experimental analysis.

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Image Super-Resolution Reconstruction Based On L1/2 Sparsity

Cited by 8 publications

References 0 publications

Compressed sensing with continuous parametric reconstruction

Compressed sensing with continuous parametric reconstruction

A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation

Experimental analysis of non-Gaussian noise resistance on global method optical flow using bilateral in reverse confidential

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