Compressed Sensing Based on the Single Layer Wavelet Transform for Image Fusion

Yang, Guangyu; Xu, Wude; Zheng, Bohong; Fang-lan, MA; Yang, Xuhui; Ma, Hongwei; Zhang, Hongxia; Han, Guoqiang

doi:10.4236/jcc.2016.415010

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compressive sensing is a promising field that has attracted recently considerable attention and has major applications ranging from image processing to wireless sensor networks, wideband spectrum sensing in cognitive radio, and channel estimation …”

Section: Introductionmentioning

confidence: 99%

A performance comparison of measurement matrices in compressive sensing

Arjoune

Kaabouch

Ghazi

et al. 2018

Int J Communication

View full text Add to dashboard Cite

Summary Compressive sensing involves 3 main processes: signal sparse representation, linear encoding or measurement collection, and nonlinear decoding or sparse recovery. In the measurement process, a measurement matrix is used to sample only the components that best represent the signal. The choice of the measurement matrix has an important impact on the accuracy and the processing time of the sparse recovery process. Hence, the design of accurate measurement matrices is of vital importance in compressive sensing. Over the last decade, a number of measurement matrices have been proposed. Therefore, a detailed review of these measurement matrices and a comparison of their performances are strongly needed. This paper explains the foundation of compressive sensing and highlights the process of measurement by reviewing the existing measurement matrices. It provides a 3‐level classification and compares the performance of 8 measurement matrices belonging to 4 different types using 5 evaluation metrics: the recovery error, processing time, recovery time, covariance, and phase transition diagram. The theoretical performance comparison is validated with experimental results, and the results show that the Circulant, Toeplitz, and Hadamard matrices outperform the other measurement matrices.

show abstract

Section: Introductionmentioning

confidence: 99%

A performance comparison of measurement matrices in compressive sensing

Arjoune

Kaabouch

Ghazi

et al. 2018

Int J Communication

View full text Add to dashboard Cite

show abstract

“…The method avoids the limitations of single wavelet transform, which can only be used for the fusion of two images. 19–20 A set-up for image acquisition and an analysis system are established for the image digitalization of nonwoven fabrics, including hardware and self-developed software embedded with the image fusion algorithms presented, based on multiwavelet transform. Related experimental works are also conducted to validate the feasibility of the method presented; final conclusions are made based on the data analysis.…”

mentioning

confidence: 99%

A novel multi-focus image fusion method of nonwovens based on GHM multiwavelet transform technology

Chen

Deng

Xin

et al. 2018

Textile Research Journal

View full text Add to dashboard Cite

In the digital optical microscope, the depth of field is limited in the image acquisition of nonwoven fabrics. The image of the fabric under the single focus image cannot achieve a uniform focus in the whole field of vision, which leads to the blurring of some regions. A new method of multi-focus image fusion based on the Geronimo-Hardin-Massopust (GHM) multiwavelet transform is introduced in this article, which realizes the digital image fusion of a sequence of images taken at different focus settings to achieve a clearer focus fusion of the fiber mesh showing the full field of view within a single image. In this article, a self-built nonwovens micro imaging system is used to collect the image sequence of the fabric at different focus images, and the initial image sequence is processed by critical sampling prefiltering. The high and low frequency components of the image are processed by different fusion rules, and the fusion image of the initial fabric is obtained through the fusion of multiwavelets and inverse transformation. Then, the fusion image of the initial fabric and the subsequent focus image are fused, using the above method, and all the fiber areas after the fusion can be clearly shown to end the operating. Experiments show that the fusion method can solve the difficulty of accurate focusing in a single image with full field of vision.

show abstract

Analysis of the Measurement Matrices for Compressive Sensing of Signals

Kulkarni¹

2023

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)

View full text Add to dashboard Cite

Compressed Sensing Based on the Single Layer Wavelet Transform for Image Fusion

Cited by 4 publications

References 12 publications

A performance comparison of measurement matrices in compressive sensing

A performance comparison of measurement matrices in compressive sensing

A novel multi-focus image fusion method of nonwovens based on GHM multiwavelet transform technology

Analysis of the Measurement Matrices for Compressive Sensing of Signals

Contact Info

Product

Resources

About