提高圖像分辨率是當(dāng)前數(shù)字圖像處理領(lǐng)域的研究熱點之一。超分辨率（SR）方法是一組信號處理算法,它允許從同一場景的單個或多個低分辨率（LR）圖像生成高分辨率（HR）圖像。不久前,深度神經(jīng)網(wǎng)絡(luò)（DNN）被引入到計算機視覺、機器翻譯、自然語言處理、語音和音頻識別、社會網(wǎng)絡(luò)分析、生物信息學(xué)、醫(yī)學(xué)圖像分析和材料檢驗等領(lǐng)域。卷積神經(jīng)網(wǎng)絡(luò)（CNN）也被廣泛應(yīng)用于彩色圖像和深度圖的超分辨率問題,在相同場景的額外HR或LR彩色圖像的引導(dǎo)下,可以從LR深度圖中恢復(fù)高分辨率的深度圖。本文提出了一種通過聯(lián)合LR深度圖和相應(yīng)的LR強度圖像重建HR深度圖的算法。為解決圖像超分辨率問題,提出了一種多尺度上采樣的聯(lián)合雙分支網(wǎng)絡(luò)（JDBNet）概念。該方法可以顯著提高恢復(fù)的HR深度圖像的質(zhì)量。網(wǎng)絡(luò)又細(xì)分為兩個獨立的網(wǎng)絡(luò)—JDBNet1和JDBNet2。JDBNet1和JDBNet2的主要區(qū)別在于,JDBNet2有兩個均方誤差損失函數(shù)作為強度Y分支的最后一個輸出層和深度映射D分支的最后一個輸出層。這進而使得JDBNet2的性能優(yōu)于JDBNet1。同一場景的低分辨率強度圖像和低分辨率深度圖是訓(xùn)練網(wǎng)絡(luò)的輸入數(shù)據(jù)。系統(tǒng)輸出數(shù)據(jù)為... 

【文章來源】：哈爾濱工業(yè)大學(xué)黑龍江省 211工程院校 985工程院校

【文章頁數(shù)】：96 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
Abbreviations
Chapter1 Introduction
    1.1 Source of the Project
    1.2 Research Background and Practical Significance of the Research
    1.3 Problem Statement
    1.4 Thesis Organization
Chapter2 Analysis of Methods for Solving Super-Resolution Problem
    2.1 General Classification of Super-Resolution Methods
    2.2 Single Image Super-Resolution
        2.2.1 Super-Resolution Methods based on Image Reconstruction Technology
        2.2.2 Super-Resolution Methods based on Machine Learning
    2.3 Multi-Frame Super-Resolution
        2.3.1 Super-Resolution Methods in Frequency Domain
        2.3.2 Super-Resolution Methods in Spatial Domain
    2.4 Research at Home and Abroad
        2.4.1 Research Abroad
        2.4.2 Research at Home
    2.5 Conclusion
Chapter3 Structure and Properties of Artificial Neural Network
    3.1 Artificial Neuron
        3.1.1 Artificial Neuron Model
        3.1.2 Activation Functions
    3.2 Artificial Neural Network
        3.2.1 Single-Layer Artificial Neural Network
        3.2.2 Multilayer Artificial Neural Network
    3.3 Convolutional Neural Network
        3.3.1 Convolutional Neural Network Structure
        3.3.2 Convolutional Neural Network Topology
    3.4 Training an Artificial Neural Networks
        3.4.1 Supervised Learning
        3.4.2 Unsupervised Learning
        3.4.3 The Process of Training Neural Network
    3.5 Loss Function
    3.6 Conclusion
Chapter4 Joint Double Branch Network
    4.1 Architecture of Network
    4.2 Formulation of JDBNet1 and JDBNet2
    4.3 Stages of Network
    4.4 Conclusion
Chapter5 Experimental Results and Analysis
    5.1 Training and Testing Details
        5.1.1 Implementation Tools
        5.1.2 Dataset Preparation
        5.1.3 Parameters of Network
    5.2 Results
    5.3 Anticipated Problems
Conclusions
References
Acknowledgements
Resume



本文編號：3469110