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                    單幅圖像超分辨率重建技術研究進展

                    張芳 趙東旭 肖志濤 耿磊 吳駿 劉彥北

                    張芳, 趙東旭, 肖志濤, 耿磊, 吳駿, 劉彥北. 單幅圖像超分辨率重建技術研究進展. 自動化學報, 2021, 47(x): 10001?10021 doi: 10.16383/j.aas.c20777
                    引用本文: 張芳, 趙東旭, 肖志濤, 耿磊, 吳駿, 劉彥北. 單幅圖像超分辨率重建技術研究進展. 自動化學報, 2021, 47(x): 10001?10021 doi: 10.16383/j.aas.c20777
                    Zhang Fang, Zhao Dong-Xu, Xiao Zhi-Tao, Geng Lei, Wu Jun, Liu Yan-Bei. Research progress of single image super-resolution reconstruction technology. Acta Automatica Sinica, 2021, 47(x): 10001?10021 doi: 10.16383/j.aas.c20777
                    Citation: Zhang Fang, Zhao Dong-Xu, Xiao Zhi-Tao, Geng Lei, Wu Jun, Liu Yan-Bei. Research progress of single image super-resolution reconstruction technology. Acta Automatica Sinica, 2021, 47(x): 10001?10021 doi: 10.16383/j.aas.c20777

                    單幅圖像超分辨率重建技術研究進展

                    doi: 10.16383/j.aas.c20777
                    基金項目: 天津市高等學校創新團隊培養計劃項目(TD13-5034)資助
                    詳細信息
                      作者簡介:

                      張芳:天津工業大學生命科學學院教授. 2009年獲得天津大學大學光學工程專業博士學位. 研究方向為圖像處理與模式識別. E-mail: hhzhangfang@126.com

                      趙東旭:天津工業大學生命科學學院碩士研究生. 2018年獲得天津工業大學電子信息工程專業學士學位. 主要研究方向為圖像處理. E-mail: zhaodongxu1028@163.com

                      肖志濤:天津工業大學生命科學學院教授. 2003 年獲得天津大學電子信息工程學院博士學位. 主要研究方向為智能信號處理, 圖像處理與模式識別. 本文通信作者. E-mail: xiaozhitao@tiangong.edu.cn

                      耿磊:天津工業大學電子與信息工程學院副教授. 2012年獲得天津大學精密儀器與光電子工程學院博士學位. 主要研究方向為圖像處理與模式識別, 智能信號處理技術與系統, DSP系統研發. E-mail: genglei@tiangong.edu.cn

                      吳駿:天津工業大學電子與信息工程學院副教授. 2007 年獲得天津大學電子信息工程學院博士學位. 主要研究方向為圖像處理與模式識別, 人工神經網絡. E-mail: wujun@tiangong.edu.cn

                      劉彥北:天津工業大學生命科學學院講師. 2017年獲得天津大學大學電路與系統專業博士學位. 研究方向為機器學習與醫學數據分析. E-mail: liuyanbei@tiangong.edu.cn

                    Research Progress of Single Image Super-resolution Reconstruction Technology

                    Funds: Supported by the Program for Innovative Research Team in University of Tianjin (Grant No. TD13-5034)
                    More Information
                      Author Bio:

                      ZHANG Fang Professor at the School of Life Science, Tiangong University. She received her Ph. D. degree in Optical Engineering from Tianjin University in 2009. Her research covers image processing and pattern recognition

                      ZHAO Dong-Xu Master candidate at the School of Life Sciences, Tiangong University. She received her bachelor degree from the School of Electronic Information Engineering, Tiangong University in 2018. The main research direction is image processing

                      XIAO Zhi-Tao Professor at the School of Life Science, Tiangong University. He received his Ph. D. degree from the School of Electronics and Information Engineering, Tianjin University in 2003. His research interest covers intelligent signal processing, image processing and pattern recognition. Corresponding author of this paper

                      GENG Lei Associate professor at the School of Life Science, Tiangong University. He received his Ph. D. degree from the School of Precision Instrument and Opto-Electronics Engineering, Tianjin University in 2012. His research interest covers image processing and pattern recognition, intelligent signal processing technology and system, DSP system research and development

                      WU Jun Associate professor at the School of Electronics and Information Engineering, Tiangong University. He received his Ph. D. degree from the School of Electronics and Information Engineering, Tianjin University in 2007. His research interest covers image processing and pattern recognition, articial neural network

                      LIU Yan-Bei Lecturer at the School of Life Science, Tiangong University. He received his Ph. D. degree in Circuit and System from Tianjin University in 2017. His research covers Machine Learning and Medical Data Analysis

                    • 摘要: 圖像分辨率是衡量一幅圖像質量的重要標準. 在軍事、醫學和安防等領域, 高分辨率圖像是專業人士分析問題并做出準確判斷的前提. 根據成像采集設備、退化因素等條件對低分辨率圖像進行超分辨率重建成為一個既具有研究價值又極具挑戰性的難點問題. 本文首先簡述了圖像超分辨率重建的概念、重建思想和方法分類; 然后重點分析用于單幅圖像超分辨率重建的空域方法, 梳理基于插值和基于學習兩大類重建方法中的代表性算法及其特點; 之后結合用于超分辨率重建技術的數據集, 重點分析比較了傳統超分辨率重建方法和基于深度學習的典型超分辨率重建方法的性能, 分析表明, 基于深度學習的超分辨率重建方法較于傳統超分辨率重建方法在準確率與魯棒性方面性能更佳; 最后對圖像超分辨率重建未來的發展趨勢進行展望.
                    • 圖  1  單幅圖像SR重建方法分類圖

                      Fig.  1  Classification of single image SR reconstruction method

                      圖  2  雙三次插值過程示意圖

                      Fig.  2  Schematic diagram of the bicubic interpolation

                      圖  3  基于深度學習的SR方法網絡結構圖

                      Fig.  3  Network structure of SR method based on deep learning

                      圖  6  基于傳統小波變換和與深度學習相結合的小波變換SR重建方法流程圖

                      Fig.  6  SR reconstruction method based on traditional wavelet transform and wavelet transform combined with deep learning

                      圖  4  (a)單尺度和(b)多尺度結構

                      Fig.  4  (a) Single-scale and (b) multi-scale structures

                      圖  5  SR重建方法本質的聯系和差異

                      Fig.  5  Relations and differences of SR reconstruction methods

                      表  1  典型深度學習網絡內部結構

                      Table  1  The internal structure of a typical deep learning network

                      方法網絡結構作用
                      VDSR[78]殘差學習(Residual-Learning)加快深度網絡收斂
                      DRCN[79]遞歸監督(Recursive-Supervision)減緩梯度爆炸或梯度消失
                      跳躍連接(Skip-Connection)存儲輸入信號用于目標預測
                      DRRN[82]全局殘差學習(LRL)學習復雜特征, 幫助梯度傳播
                      局部殘差學習(GRL)攜帶豐富的細節信息
                      遞歸塊(Recursive Block)權值共享, 多路徑遞歸連接
                      SRDenseNet[83]密集跳躍連接(Dense Skip Connection)增強不同層間的特征融合
                      EDSR[91]殘差塊(Residual Block)增強初始層級與深度層級的聯系;
                      MemNet[85]內存塊(Memory Block)自適應地學習不同內存的不同權重;
                      遞歸單元(Recursive Units)控制應該保留多少長期內存;
                      門單元(Gate Units)存儲多少短期內存
                      RDN[86]殘差密集塊(Residual Dense Block, RDB)讀取前一個RDN狀態, 增強層間連接
                      連續記憶機制全局特征融合, 挖掘分層信息
                      SRFBN[96]反饋塊(Feedback Block, FB); 共享權重, 幫助更好的高級信息表達;
                      反饋機制(Feedback Mechanism)高級信息回傳給低級信息
                      RCAN[99]通道注意力機制(Attention Mechanism)分級標定圖像低級和高級語義信息
                      下載: 導出CSV

                      表  2  SR網絡輸入及層數對照表

                      Table  2  SR network input and layer number comparison

                      方法網絡輸入網絡層數
                      SRCNNLR+BI3
                      FSRCNNLR8
                      ESPCNLR3
                      VDSRLR+BI20
                      DRCNLR+BI20
                      LapSRNLR27
                      REDLR30
                      DRRNLR+BI52
                      SRDenseNetLR64
                      SRGANLR+BI54
                      MemNetLR+BI80
                      RDNLR20(RDB)
                      下載: 導出CSV

                      表  3  SR重建圖像常用質量評價方法

                      Table  3  Common quality evaluation methods for SR reconstructed images

                      特點類別常用評估方法名稱適用場景優缺點使用方法
                      主觀全參考基于評分MOS/DMOS不受距離、設備、光照、及觀測者的視覺能力、情緒等因素影響的情況優: 能夠真實的反映圖像的直觀質量, 評價結果可靠, 無技術障礙.缺: 無法應用數學模型對其進行描述, 耗時多、費用高. 易受觀測動機、觀測環境等諸多因素的影響.根據評分表分別對參考圖像和待測圖像評分
                      客觀全參考
                      (真值圖像+失真圖像)
                      基于像素MSE/PSNR參考圖像完整的情況優: 計算形式上非常簡單, 物理意義理解也很清晰.缺: 未考慮將人類視覺系統特性, 單純從數學角度來分析差異, 未與圖像的感知質量產生聯系.所有像素點對應比較
                      基于人類視覺系統(結構和特征)SSIM/ MS-SSIM/
                      FSIM/VIF/IFC
                      優: 從整體上直接模擬HVS(人類視覺系統)抽取對象結構的人類視覺功能, 更符合視覺感知.缺: 從圖像像素值的全局統計出發, 未考慮人眼的局部視覺因素, 對于圖像局部質量無從把握.
                      基于深度學習NAR-DCNN[145]優: 直接從原始圖像像素學習判別圖像特征, 而不使用手工提取特征. 共性: 首先對理想圖像的特征做出某種假設, 轉化成一個分類或回歸問題; 再為該假設建立相應的數學分析模型, 學習特征; 最后通過計算待評圖像在該模型下的表現特征, 從而得到圖像的質量評價結果.
                      盲參考
                      (失真圖像)
                      基于感知/概率模型PI[146]/Ma[147]/
                      NIQE[148]/
                      BLIINDS[149]/
                      BIQI[150]/
                      BRISQUE[150]
                      無參考圖像的情況. 無需參考圖像, 靈活性強. 特征由自然場景統計提取
                      基于深度學習
                      (網絡模型)
                      LPIPS[151]/
                      DB-CNN[152]
                      RankIQA[153]/
                      DIQI[154]
                      CNN/CNN+回歸模型提取特征
                      下載: 導出CSV
                      360彩票
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