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
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湖南科技大學(xué)信息與電氣工程學(xué)院 湘潭 411100
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中南大學(xué)自動(dòng)化學(xué)院 長(cháng)沙 410000
基金項目: 國家重點(diǎn)研發(fā)計劃“政府間國際創(chuàng )新合作”重點(diǎn)專(zhuān)項(2019YFE0118700), 國家自然科學(xué)基金(62222306, 61973110, 62203164), 湖南省教育廳科學(xué)研究項目(22A0349, 21B0499)資助
Fast Enhancement Method for Low Light Images Guided by Retinex Prior
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School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411100
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School of Automation, Central South University, Changsha 410000
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)
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摘要: 低光照圖像增強旨在提高在低光照環(huán)境下所采集圖像的視覺(jué)質(zhì)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(jué)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(wèn)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(huì )平滑細節紋理的特點(diǎn), 提出基于域變換遞歸濾波的Gamma校正圖優(yōu)化方法, 降低輸出圖像噪聲的同時(shí)保持顏色和對比度. 實(shí)驗結果表明, 所提方法不僅在主客觀(guān)圖像質(zhì)量評價(jià)上優(yōu)于現有大多數主流算法, 而且在計算效率上具有十分顯著(zhù)的優(yōu)勢.Abstract: Low light image enhancement aims to improve the visual quality of images captured in low light environment. However, existing low light image enhancement methods are difficult to achieve a good balance between computational efficiency and enhancement performance. Therefore, a fast low light image enhancement method guided by Retinex prior is proposed. The Retinex model is combined with Gamma correction to quickly output images with high contrast, good visual effects and low noise. To obtain images with good illumination and guide the determination of Gamma correction map with the same size as the input image, a prior image generation method based on Retinex model is proposed. To solve the problem of color distortion in extremely low illumination areas, a fusion-based Gamma correction map estimation method is proposed, which uses arctangent transform to restore the color and contrast of extremely low illumination areas to improve the enhancement performance of Gamma correction map in extremely low illumination areas. To suppress the noise of the output image, considering that the completely smooth Gamma correction map cannot smooth the detail texture, an optimization method of Gamma correction map based on domain transform recursive filtering is proposed, which can reduce the noise and preserve the color and contrast of the output image. The experimental results show that the proposed method not only outperforms most of the existing mainstream algorithms in subjective and objective image quality evaluation, but also has significant advantages in computational efficiency.
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Key words:
- Low light image enhancement /
- Gamma correction map /
- Retinex model /
- noise suppression
1)1 1https://sites.google.com/site/vonikakis/datasets -
圖 4 先驗圖像與增強圖像之間優(yōu)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(xiàn)
Fig. 5 Weight function curves with different values of $ \alpha $
圖 6 室內場(chǎng)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(chǎng)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(huán)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(shí)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 DCIM 3.399 3.358 3.145 3.109 3.162 3.185 3.063 Fusion 3.405 3.399 3.428 3.405 3.387 3.340 3.358 NPE 3.784 3.478 3.499 3.497 3.484 3.523 3.429 VV 3.485 3.512 3.342 3.321 3.316 3.345 3.258 LIME 3.426 3.490 3.181 3.184 3.046 3.193 2.990 Darkface 3.198 3.246 2.779 2.716 2.438 2.407 2.496 平均值 3.449 3.413 3.229 3.205 3.139 3.165 3.099 
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 DCIM 753.2 857.5 782.2 770.0 590.9 777.6 581.0 Fusion 564.9 803.6 614.2 681.7 527.4 703.1 523.0 NPE 731.2 847.6 750.1 845.2 549.3 849.7 588.1 VV 563.3 799.3 517.3 596.5 369.3 619.8 347.6 LIME 540.9 608.8 661.0 687.4 537.9 678.1 557.1 Darkface 955.8 942.1 883.9 428.6 621.8 458.9 260.8 平均值 684.9 809.9 701.4 668.2 532.7 681.2 476.2
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 DCIM 1.156 1.707 0.578 0.485 0.491 1.099 0.358 Fusion 0.561 0.715 0.509 0.599 0.597 0.510 0.325 NPE 0.778 0.908 0.690 0.685 0.688 0.623 0.526 VV 0.764 0.836 0.549 0.637 0.663 0.539 0.459 LIME 1.036 1.074 0.872 0.733 0.722 0.744 0.619 Darkface 2.019 2.586 1.585 1.157 1.147 1.084 0.789 平均值 1.052 1.304 0.797 0.716 0.718 0.766 0.512
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表 4 不同方法處理不同圖像尺寸的運行時(shí)間 (s)
Table 4 Running time for different image sizes processed by different methods (s)
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