Super resolution of remote sensing image based on structure similarity in CS frame

Pan, Zongxu; Huang, Huijuan; Sun, Weidong

doi:10.1117/12.902613

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, spatial-resolution enhancement and super resolution are critical in remote sensing, especially in satellite imagery and far-field imaging, where the aim is to capture and recognize targets having a size approaching or below the spatial resolution limit provided by the imaging system. Some examples allowing super resolution in remotesensing imagery are found to succeed with image restoration [23], shifting the set of low-resolution images [24,25], compressive sensing [26], and using turbulences [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Passive time-multiplexing super-resolved technique for axially moving targets

et al. 2013

View full text Add to dashboard Cite

In this paper we present a super-resolving approach for detecting an axially moving target that is based upon a time-multiplexing concept and that overcomes the diffraction limit set by the optics of an imaging camera by a priori knowledge of the high-resolution background in front of which the target is moving. As the movement trajectory is axial, the approach can be applied to targets that are approaching or moving away from the camera. By recording a set of low-resolution images at different target axial positions, the super-resolving algorithm weights each image by demultiplexing them using the high-resolution background image and provides a super-resolved image of the target. Theoretical analyses as well as simulations and preliminary experimental validation are presented to validate the proposed approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Passive time-multiplexing super-resolved technique for axially moving targets

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The reconstruction method in [17] modified the algorithm of Yang [6][7][8] with the CS theory, reducing the number of training dictionary but still using external several training images. Pan in [15][16] and Zhu in [18] modified the method in [17] not relying on any external images, assuming that the low resolution patches can be considered as the compressed sensing version of high resolution patches under the framework of CS theory.…”

Section: Introductionmentioning

confidence: 99%

“…Example-based SR approaches break the limitations existing in the traditional reconstruction-based algorithms. They learn the mapping relationship between the corresponding pre-processed low and high resolution training samples to recover the missed HF details, mainly including learningbased approach [4], neighborhood embedding approach [5] and sparse representation methods [6][7][8][9][10][11][12][13][14][15][16][17][18], etc.…”

Section: Introductionmentioning

confidence: 99%