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Sinusoidal Sampling Enhanced Compressive Camera for High Speed Imaging

Compressive sensing technique allows capturing fast phenomena at a much higher frame rate than the camera sensor, by recovering a frame sequence from their encoded combination. However, most conventional compressive video sensing methods limit the achieved frame rate improvement to tenfold and only support low resolution recovery. Making use of the camera's redundant spatial resolution for further frame rate improve, here we report a novel compressive video acquisition technique termed Sinusoidal Sampling Enhanced Compressive Camera (S2EC2) to encode denser frames within a snapshot. Specifically, we decompose the dense frames into groups and apply combinational coding: random codes within each group for compressive acquisition; group specific sinusoidal codes to multiplex different groups onto the high resolution sensor. The sinusoidal codes designed for these groups would shift their frequency components by different offsets in the Fourier domain and staggered the dominant frequencies of the coded measurements of these groups. Correspondingly, the reconstruction successfully separate coded measurements of different groups and recovers frames within each group. Besides, we also solve the implementation problem of insufficient gray scale spatial light modulation speed, and build a prototype achieving 2000 fps reconstruction with a 15.6 fps camera (the actual compression ratio is 0.009). The extensive experiments validate the proposed approach.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.4 April 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1380 - 1393

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

IMAGE PROCESSING
HIGH-SPEED VIDEO
COMPRESSIVE SENSING
COMPUTATIONAL IMAGING
VIDEO PROCESSING
COMPRESSIVE VIDEO
SINUSOIDAL CODING

Info Detail Spesifik

DOI: 10.1109/TPAMI.2019.2946567

Pernyataan Tanggungjawab

Chao Deng, Yuanlong Zhang, Yifeng Mao, Jingtao Fan, Jinli Suo, Zhili Zhang, Qionghai Dai

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