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基于多通道憶阻脈沖耦合神經(jīng)網(wǎng)絡(luò)的多幀圖像超分辨率重建算法

董哲康 杜晨杰 林輝品 賴俊昇 胡小方 段書凱

董哲康, 杜晨杰, 林輝品, 賴俊昇, 胡小方, 段書凱. 基于多通道憶阻脈沖耦合神經(jīng)網(wǎng)絡(luò)的多幀圖像超分辨率重建算法[J]. 電子與信息學(xué)報(bào), 2020, 42(4): 835-843. doi: 10.11999/JEIT190868
引用本文: 董哲康, 杜晨杰, 林輝品, 賴俊昇, 胡小方, 段書凱. 基于多通道憶阻脈沖耦合神經(jīng)網(wǎng)絡(luò)的多幀圖像超分辨率重建算法[J]. 電子與信息學(xué)報(bào), 2020, 42(4): 835-843. doi: 10.11999/JEIT190868
Zhekang DONG, Chenjie DU, Huipin Lin, Chun sing LAI, Xiaofang HU, Shukai DUAN. Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 835-843. doi: 10.11999/JEIT190868
Citation: Zhekang DONG, Chenjie DU, Huipin Lin, Chun sing LAI, Xiaofang HU, Shukai DUAN. Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 835-843. doi: 10.11999/JEIT190868

基于多通道憶阻脈沖耦合神經(jīng)網(wǎng)絡(luò)的多幀圖像超分辨率重建算法

doi: 10.11999/JEIT190868
基金項(xiàng)目: 國家自然科學(xué)基金(61571394, 61601376),浙江省屬高?;究蒲袠I(yè)務(wù)費(fèi)項(xiàng)目(GK199900299012-010)
詳細(xì)信息
    作者簡介:

    董哲康:男,1989年生,副教授,主要研究方向?yàn)閼涀枥碚?、基于憶阻器的神?jīng)形態(tài)系統(tǒng)研究

    杜晨杰:男,1990年生,博士生,研究方向?yàn)閼涀枥碚摗⒒趹涀杵鞯纳窠?jīng)形態(tài)系統(tǒng)研究

    林輝品:男,1987年生,講師,主要研究方向?yàn)閼涀枥碚?、基于憶阻器的神?jīng)形態(tài)系統(tǒng)研究

    賴俊昇:男,1991年生,助理教授,主要研究方向?yàn)閼涀枥碚?、基于憶阻器的神?jīng)形態(tài)系統(tǒng)研究

    胡小方:女,1984年生,副教授,主要研究方向?yàn)閼涀杵骼碚摗⒒趹涀杵鞯姆蔷€性系統(tǒng)研究

    段書凱:男,1973年生,教授,主要研究方向?yàn)閼涀杵骼碚?、微納系統(tǒng)研究

    通訊作者:

    林輝品 linhuipin@hdu.edu.cn

  • 中圖分類號: TN601; TN911.73

Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm

Funds: The National Natural Science Foundation of China (61571394, 61601376), The Fundamental Research Funds for the Provincial Universities (GK199900299012-010)
  • 摘要: 高清晰度的圖像是信息獲取和精確分析的前提,研究多幀圖像的超分辨率重建能夠有效解決因外部拍攝環(huán)境引起的圖像細(xì)節(jié)丟失、邊緣模糊等問題。該文基于納米級憶阻器,設(shè)計(jì)一種多通道憶阻脈沖耦合神經(jīng)網(wǎng)絡(luò)模型(MMPCNN),能夠有效模擬網(wǎng)絡(luò)中連接系數(shù)的動態(tài)變化,解決神經(jīng)網(wǎng)絡(luò)中固有的參數(shù)估計(jì)問題。同時,將提出的網(wǎng)絡(luò)應(yīng)用于多幀圖像超分辨率重建中,實(shí)現(xiàn)低分辨率配準(zhǔn)圖像的融合操作,并通過基于稀疏編碼的單幀圖像超分辨率重構(gòu)算法對獲得的初始高分辨率圖像進(jìn)行優(yōu)化。最終,一系列計(jì)算機(jī)仿真及分析(主觀/客觀分析)驗(yàn)證了該文提出方案的正確性和有效性。
  • 圖  1  VTEAM憶阻器仿真結(jié)果

    圖  2  多通道憶阻脈沖耦合神經(jīng)網(wǎng)絡(luò)MMPCNN

    圖  3  圖像降質(zhì)模型

    圖  4  提出的圖像超分辨率算法流程圖

    圖  5  多幀圖像超分辨重構(gòu)的實(shí)現(xiàn)流程

    圖  6  對比實(shí)驗(yàn)仿真結(jié)果

    圖  7  對比實(shí)驗(yàn)仿真結(jié)果

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出版歷程
  • 收稿日期:  2019-11-01
  • 修回日期:  2020-01-12
  • 網(wǎng)絡(luò)出版日期:  2020-02-12
  • 刊出日期:  2020-06-04

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