A novel region-based image-fusion framework for compressive imaging (CI) and its implementation scheme are proposed. Unlike previous works on conventional image fusion, we consider both compression capability on sensor side and intelligent understanding of the image contents in the image fusion. Firstly, the compressed sensing theory and normalized cut theory are introduced. Then region-based image-fusion framework for compressive imaging is proposed and its corresponding fusion scheme is constructed. Experiment results demonstrate that the proposed scheme delivers superior performance over traditional compressive image-fusion schemes in terms of both object metrics and visual quality
Compressive imaging is an emerging field which allows one to acquire far fewer measurements of a sce...
The aim of image compression endeavour is to reduce the total data required to represent the image, ...
<p> In this letter, we propose a novel remote sensing image fusion method based on the non-subsampl...
Abstract. Compressive sensing is a novel information theory proposed recently.It broke through the r...
This paper addresses a novel method of image fusion problem for different application scenarios, emp...
As the compressed sensing theory can offer a better performance than Nyquist sampling theorem when d...
International audienceCompressive spectral imagers reduce the number of sampled pixels by coding and...
This thesis develops algorithms and applications for compressive sensing, a topic in signal processi...
Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simult...
We propose a novel super-resolution multisource images fusion scheme via compressive sensing and dic...
<p> Compressive Sensing (CS) is a new sampling framework that provides an alternative to the well-k...
Computational imaging becomes a cutting edge research area by incorporating signal/image processing ...
his paper provides clustered compressive sensing (CCS) based image processing using Bayesian framewo...
Compressive sensing (CS) has demonstrated the ability in the field of signal and image processing to...
In this paper, we propose a novel compressive imaging framework for color images. We first introduce...
Compressive imaging is an emerging field which allows one to acquire far fewer measurements of a sce...
The aim of image compression endeavour is to reduce the total data required to represent the image, ...
<p> In this letter, we propose a novel remote sensing image fusion method based on the non-subsampl...
Abstract. Compressive sensing is a novel information theory proposed recently.It broke through the r...
This paper addresses a novel method of image fusion problem for different application scenarios, emp...
As the compressed sensing theory can offer a better performance than Nyquist sampling theorem when d...
International audienceCompressive spectral imagers reduce the number of sampled pixels by coding and...
This thesis develops algorithms and applications for compressive sensing, a topic in signal processi...
Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simult...
We propose a novel super-resolution multisource images fusion scheme via compressive sensing and dic...
<p> Compressive Sensing (CS) is a new sampling framework that provides an alternative to the well-k...
Computational imaging becomes a cutting edge research area by incorporating signal/image processing ...
his paper provides clustered compressive sensing (CCS) based image processing using Bayesian framewo...
Compressive sensing (CS) has demonstrated the ability in the field of signal and image processing to...
In this paper, we propose a novel compressive imaging framework for color images. We first introduce...
Compressive imaging is an emerging field which allows one to acquire far fewer measurements of a sce...
The aim of image compression endeavour is to reduce the total data required to represent the image, ...
<p> In this letter, we propose a novel remote sensing image fusion method based on the non-subsampl...