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              基于Retinex先驗引導的低光照圖像快速增強方法

              何磊 易遵輝 謝永芳 陳超洋 盧明

              何磊, 易遵輝, 謝永芳, 陳超洋, 盧明. 基于Retinex先驗引導的低光照圖像快速增強方法. 自動化學報, 2024, 50(5): 1035?1046 doi: 10.16383/j.aas.c230585
              引用本文: 何磊, 易遵輝, 謝永芳, 陳超洋, 盧明. 基于Retinex先驗引導的低光照圖像快速增強方法. 自動化學報, 2024, 50(5): 1035?1046 doi: 10.16383/j.aas.c230585
              He Lei, Yi Zun-Hui, Xie Yong-Fang, Chen Chao-Yang, Lu Ming. Fast enhancement method for low light images guided by Retinex prior. Acta Automatica Sinica, 2024, 50(5): 1035?1046 doi: 10.16383/j.aas.c230585
              Citation: He Lei, Yi Zun-Hui, Xie Yong-Fang, Chen Chao-Yang, Lu Ming. Fast enhancement method for low light images guided by Retinex prior. Acta Automatica Sinica, 2024, 50(5): 1035?1046 doi: 10.16383/j.aas.c230585

              基于Retinex先驗引導的低光照圖像快速增強方法

              doi: 10.16383/j.aas.c230585
              基金項目: 國家重點研發計劃“政府間國際創新合作”重點專項(2019YFE0118700), 國家自然科學基金(62222306, 61973110, 62203164), 湖南省教育廳科學研究項目(22A0349, 21B0499)資助
              詳細信息
                作者簡介:

                何磊:湖南科技大學信息與電氣工程學院講師. 2017年和2023年分別獲得山東大學學士學位和中南大學博士學位. 主要研究方向為視覺檢測, 圖像處理和深度學習. E-mail: helei_xb@hnust.edu.cn

                易遵輝:湖南科技大學信息與電氣工程學院講師. 2017年和2023年分別獲得山東大學學士學位和中南大學博士學位. 主要研究方向為光學成像, 圖像處理和視覺檢測. 本文通信作者. E-mail: yizunhui@hnust.edu.cn

                謝永芳:中南大學自動化學院教授. 1999年獲得中南工業大學博士學位. 主要研究方向為分散控制與魯棒控 制, 過程控制, 工業大數據和知識自動化. E-mail: yfxie@csu.edu.cn

                陳超洋:湖南科技大學信息與電氣工程學院教授. 2014年獲得華中科技大學博士學位. 主要研究方向為群機器人系統協同控制, 復雜網絡研究. E-mail: ouzk@163.com

                盧明:湖南科技大學信息與電氣工程學院教授. 2014年獲得中南大學博士學位. 主要研究方向為流程工業工況識別與智能優化控制, 機器視覺與智能機器人. E-mail: mlu@hnust.edu.cn

              Fast Enhancement Method for Low Light Images Guided by Retinex Prior

              Funds: Supported by National Key Research and Development Program of China for International Scientific and Technological Cooperation Projects (2019YFE0118700), National Natural Science Foundation of China (62222306, 61973110, 62203164), and Scientific Research Fund of Hunan Provincial Education Department (22A0349, 21B0499)
              More Information
                Author Bio:

                HE Lei Lecturer at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his bachelor degree from Shandong University in 2017 and Ph.D. degree from Central South University in 2023. His research interest covers vision-based measurement, image processing, and deep learning

                YI Zun-Hui Lecturer at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his bachelor degree from Shandong University in 2017 and Ph.D. degree from Central South University in 2023. His research interest covers optical imaging, image processing, and vision-based measurement. Corresponding author of this paper

                XIE Yong-Fang Professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University in 1999. His research interest covers decentralized control and robust control, process control, industrial big data, and knowledge automation

                CHEN Chao-Yang Professor at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his Ph.D. degree from Huazhong University of Science and Technology in 2014. His research interest covers cooperative control of swarm robot systems and complex network research

                LU Ming Professor at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his Ph.D. degree from Central South University in 2014. His research interest covers working condition recognition and intelligent optimization control of process industry, machine vision and intelligent robot

              • 摘要: 低光照圖像增強旨在提高在低光照環境下所采集圖像的視覺質量. 然而, 現有的低光照圖像增強方法難以在計算效率與增強性能之間達到很好的平衡, 為此, 提出一種基于Retinex先驗引導的低光照圖像快速增強方法, 將Retinex模型與Gamma校正相結合, 快速輸出具有對比度高、視覺效果好和低噪聲的圖像. 為獲取具有良好光照的圖像以引導確定與輸入圖像尺寸大小一致的Gamma校正圖, 提出基于Retinex模型的先驗圖像生成方法. 針對所提先驗圖像生成方法在極低光照區域中存在顏色失真的問題, 提出一種基于融合的Gamma校正圖估計方法, 采用反正切變換恢復極低光照區域的顏色和對比度, 以提升Gamma校正圖在極低光照區域的增強性能. 為抑制輸出圖像的噪聲, 考慮到完全平滑的Gamma校正圖不會平滑細節紋理的特點, 提出基于域變換遞歸濾波的Gamma校正圖優化方法, 降低輸出圖像噪聲的同時保持顏色和對比度. 實驗結果表明, 所提方法不僅在主客觀圖像質量評價上優于現有大多數主流算法, 而且在計算效率上具有十分顯著的優勢.
                1)  11 https://sites.google.com/site/vonikakis/datasets
              • 圖  1  不同方法的低光照增強結果

                Fig.  1  Low light enhancement results of different methods

                圖  2  所提方法的算法流程框架

                Fig.  2  The algorithm flow framework of the proposed method

                圖  3  獲取先驗圖像的示例

                Fig.  3  Example of obtaining the prior image

                圖  4  先驗圖像與增強圖像之間優勢和劣勢的可視化

                Fig.  4  Visualization of advantages and disadvantages between prior image and enhanced image

                圖  5  不同$ \alpha $取值下的權重函數曲線

                Fig.  5  Weight function curves with different values of $ \alpha $

                圖  6  室內場景下不同方法的低光照圖像增強結果

                Fig.  6  Low light image enhancement results of different methods in indoor scene

                圖  7  室外場景下不同方法的低光照圖像增強結果

                Fig.  7  Low light image enhancement results of different methods in outdoor scene

                圖  8  黑夜環境下不同方法的低光照圖像增強結果

                Fig.  8  Low light image enhancement results of different methods under dark night environment

                圖  9  針對真實高噪聲低光照圖像下不同方法的增強結果

                Fig.  9  Enhancement results of different methods for real high-noise low light image

                圖  10  所提方法不同模塊的消融研究

                Fig.  10  Ablation studies of different modules of the proposed method

                表  1  不同方法在不同低光照數據集中的CEIQ值

                Table  1  CEIQ values of different methods in different low light datasets

                NPEA[34]LIME[8]MF[9]LECARM[35]STAR[36]SCI[16]所提方法
                DCIM3.3993.3583.1453.1093.1623.1853.063
                Fusion3.4053.3993.4283.4053.3873.3403.358
                NPE3.7843.4783.4993.4973.4843.5233.429
                VV3.4853.5123.3423.3213.3163.3453.258
                LIME3.4263.4903.1813.1843.0463.1932.990
                Darkface3.1983.2462.7792.7162.4382.4072.496
                平均值3.4493.4133.2293.2053.1393.1653.099
                下載: 導出CSV

                表  2  不同方法在不同低光照數據集中的LOE值

                Table  2  LOE values of different methods in different low light datasets

                NPEA[34]LIME[8]MF[9]LECARM[35]STAR[36]SCI[16]所提方法
                DCIM753.2857.5782.2770.0590.9777.6581.0
                Fusion564.9803.6614.2681.7527.4703.1523.0
                NPE731.2847.6750.1845.2549.3849.7588.1
                VV563.3799.3517.3596.5369.3619.8347.6
                LIME540.9608.8661.0687.4537.9678.1557.1
                Darkface955.8942.1883.9428.6621.8458.9260.8
                平均值684.9809.9701.4668.2532.7681.2476.2
                下載: 導出CSV

                表  3  不同方法在不同低光照數據集中的NL值

                Table  3  NL values of different methods in different low light datasets

                NPEA[34]LIME[8]MF[9]LECARM[35]STAR[36]SCI[16]所提方法
                DCIM1.1561.7070.5780.4850.4911.0990.358
                Fusion0.5610.7150.5090.5990.5970.5100.325
                NPE0.7780.9080.6900.6850.6880.6230.526
                VV0.7640.8360.5490.6370.6630.5390.459
                LIME1.0361.0740.8720.7330.7220.7440.619
                Darkface2.0192.5861.5851.1571.1471.0840.789
                平均值1.0521.3040.7970.7160.7180.7660.512
                下載: 導出CSV

                表  4  不同方法處理不同圖像尺寸的運行時間 (s)

                Table  4  Running time for different image sizes processed by different methods (s)

                NPEA[34]LIME[8]MF[9]LECARM[35]STAR[36]所提方法
                480×3203.710.1200.2640.1014.860.096
                640×4807.190.2730.3350.21410.120.161
                960×72016.250.5270.5160.39221.410.325
                1280×720 18.010.6770.6030.46324.630.417
                1920×108048.331.3481.3210.98964.320.935
                下載: 導出CSV
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                        亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页
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                        • 收稿日期:  2023-09-19
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