A Biological Retina Inspired Tone Mapping Processor for High-Speed and Energy-Efficient Image Enhancement.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
30 Sep 2020
Historique:
received: 20 07 2020
revised: 18 09 2020
accepted: 25 09 2020
entrez: 3 10 2020
pubmed: 4 10 2020
medline: 7 4 2021
Statut: epublish

Résumé

In this work, a biological retina inspired tone mapping processor for high-speed and energy-efficient image enhancement has been proposed. To achieve high throughput and high energy efficiency, several hardware design techniques have been proposed, including data partition based parallel processing with S-shape sliding, adjacent frame feature sharing, multi-layer convolution pipelining, and convolution filter compression with zero skipping convolution. Implemented on a Xilinx's Virtex7 FPGA, the proposed design achieves a high throughput of 189 frames per second for 1024 × 768 RGB images while consuming 819 mW. Compared with several state-of-the-art tone mapping processors, the proposed design shows higher throughput and energy efficiency. It is suitable for high-speed and energy-constrained image enhancement applications.

Identifiants

pubmed: 33007833
pii: s20195600
doi: 10.3390/s20195600
pmc: PMC7583778
pii:
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Science & Technology Supporting Program of Sichuan Province
ID : 2018GZDZX0003
Organisme : National Key R&D Program of China
ID : 2018YFB2101300

Références

IEEE Trans Image Process. 1997;6(7):965-76
pubmed: 18282987
IEEE Trans Pattern Anal Mach Intell. 2011 Oct;33(10):2002-12
pubmed: 21383397

Auteurs

Xiaoqiang Xiang (X)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Lili Liu (L)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Luying Que (L)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Conghan Jia (C)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Bo Yan (B)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Yongjie Li (Y)

School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Jinhong Guo (J)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

Jun Zhou (J)

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China.

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Classifications MeSH