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Computational Imaging System

Compressive Image Recovery using Recurrent Generative Model


Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well.

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Authors:
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra
Submitted On:
26 September 2017 - 7:44am
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icip17_final.pptx

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[1] Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra, "Compressive Image Recovery using Recurrent Generative Model", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2246. Accessed: Oct. 23, 2017.
@article{2246-17,
url = {http://sigport.org/2246},
author = {Akshat Dave; Anil Kumar Vadathya; Kaushik Mitra },
publisher = {IEEE SigPort},
title = {Compressive Image Recovery using Recurrent Generative Model},
year = {2017} }
TY - EJOUR
T1 - Compressive Image Recovery using Recurrent Generative Model
AU - Akshat Dave; Anil Kumar Vadathya; Kaushik Mitra
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2246
ER -
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. (2017). Compressive Image Recovery using Recurrent Generative Model. IEEE SigPort. http://sigport.org/2246
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra, 2017. Compressive Image Recovery using Recurrent Generative Model. Available at: http://sigport.org/2246.
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. (2017). "Compressive Image Recovery using Recurrent Generative Model." Web.
1. Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. Compressive Image Recovery using Recurrent Generative Model [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2246

Fitness Heart Rate Measurement Using Face Videos


Recent studies showed that subtle changes in human’s face color due to the heartbeat can be captured by digital video recorders. Most work focused on still/rest cases or those with relatively small motions. In this work, we propose a heart-rate monitoring method for fitness exercise videos. We focus on building a highly precise motion compensation scheme with the help of the optical flow, and use motion information as a cue to adaptively remove ambiguous frequency components for improving the heart rates estimates.

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Authors:
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu
Submitted On:
19 October 2017 - 11:04am
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Presentation slides (pdf version)

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Presentation slides (ppt version)

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[1] Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu, "Fitness Heart Rate Measurement Using Face Videos", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2244. Accessed: Oct. 23, 2017.
@article{2244-17,
url = {http://sigport.org/2244},
author = {Qiang Zhu; Chau-Wai Wong; Chang-Hong Fu; Min Wu },
publisher = {IEEE SigPort},
title = {Fitness Heart Rate Measurement Using Face Videos},
year = {2017} }
TY - EJOUR
T1 - Fitness Heart Rate Measurement Using Face Videos
AU - Qiang Zhu; Chau-Wai Wong; Chang-Hong Fu; Min Wu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2244
ER -
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu. (2017). Fitness Heart Rate Measurement Using Face Videos. IEEE SigPort. http://sigport.org/2244
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu, 2017. Fitness Heart Rate Measurement Using Face Videos. Available at: http://sigport.org/2244.
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu. (2017). "Fitness Heart Rate Measurement Using Face Videos." Web.
1. Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu. Fitness Heart Rate Measurement Using Face Videos [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2244

LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR


Mobile cameras have come a long way since their evolution and have replaced digital still cameras. However, their lowlight photography performance needs significant improvement. Dual camera systems consisting of a monochrome sensor and a Bayer sensor offer us a way to improve the low-light photography. The existing dual camera systems use post-processing methods after Image Signal Processor (ISP) for image fusion which are computationally intensive and use two ISPs. We propose a novel architecture in which the image fusion can be done in Bayer domain prior to the ISP.

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Authors:
Aravind Rangavajjula, Anurag Jain
Submitted On:
12 September 2017 - 3:19am
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ICIP_2017_Paper_#1351_Presentation_Prashant_Rupapara.pdf

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[1] Aravind Rangavajjula, Anurag Jain, "LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1925. Accessed: Oct. 23, 2017.
@article{1925-17,
url = {http://sigport.org/1925},
author = {Aravind Rangavajjula; Anurag Jain },
publisher = {IEEE SigPort},
title = {LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR},
year = {2017} }
TY - EJOUR
T1 - LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR
AU - Aravind Rangavajjula; Anurag Jain
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1925
ER -
Aravind Rangavajjula, Anurag Jain. (2017). LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR. IEEE SigPort. http://sigport.org/1925
Aravind Rangavajjula, Anurag Jain, 2017. LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR. Available at: http://sigport.org/1925.
Aravind Rangavajjula, Anurag Jain. (2017). "LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR." Web.
1. Aravind Rangavajjula, Anurag Jain. LOW COMPLEXITY IMAGE FUSION IN BAYER DOMAIN USING A MONOCHROME SENSOR AND BAYER SENSOR [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1925