An Underwater Image Enhancement Algorithm Based on Improved MSRCR-CLAHE Fusion
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摘要: 针对海洋复杂成像环境导致的水下图像出现颜色衰退、对比度低等问题，提出一种改进的带色彩恢复的多尺度视网膜（Multi-Scale Retinex with Color Restore，MSRCR）与限制对比度自适应直方图均衡化（Contrast Limited Adaptive Histogram Equalization，CLAHE）多尺度融合的水下图像增强算法。首先，采用带有导向滤波的MSRCR算法解决水下图像颜色衰退的问题；其次，采用带有Gamma校正的CLAHE算法以提高水下图像的对比度；最后，对经过改进的MSRCR和CLAHE处理后的图像进行多尺度融合以获得细节增强后的水下图像。实验结果表明，和其他算法相比，文中算法的峰值信噪比（Peak Signal to Noise Ratio，PSNR）平均提高了9.3914、结构相似性（Structural Similarity Index Measure，SSIM）平均提高了0.3013、水下图像评价指标（Underwater Image Quality Evaluation，UIQE）平均提高了4.7047，能实现水下图像的有效增强。Abstract: To address the problems of color fading and low contrast in underwater images caused by the complex imaging environment in the ocean, improved Multi-Scale Retinex with Color Restore (MSRCR) and Contrast Limited Adaptive Histogram Equalization (CLAHE) multi-scale fusion algorithms for underwater image enhancement are proposed. First, the MSRCR algorithm with guided filtering was used to solve the problem of underwater image color fading. Second, the CLAHE algorithm with Gamma correction was used to improve the contrast of underwater images. Finally, the improved MSRCR and CLAHE images were fused at multi-scale to obtain an underwater image with enhanced detail. The experimental results show that, compared with other algorithms, the Peak Signal-To-Noise Ratio (PSNR) of the proposed algorithm is improved by 9.3914 on average, and the Structural Similarity Index Measure (SSIM) and Underwater Image Quality Evaluation (UIQE) increased by 0.3013 and 4.7047 on average, respectively, which can realize the effective enhancement of underwater images.
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Keywords:
- underwater image enhancement /
- CLAHE enhancement /
- MSRCR /
- guided filtering /
- Gamma correction /
- multi-scale fusion
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图 4 不同直方图均衡算法的处理结果
Figure 4. Processing results of different histogram equalization algorithms
表 1 不同算法PSNR性能比较
Table 1 PSNR performance comparison of different algorithms
PNSR Original Reference[3] Reference[7] Reference[13] Reference[16] Ours Picture 1 - 13.8014 14.2261 16.3929 21.0436 24.2896 Picture 2 - 6.2876 6.7461 6.2519 14.0142 18.9873 Picture 3 - 15.9585 12.5442 13.0328 20.9045 22.3212 Picture 4 - 7.3254 7.9521 8.1265 13.2158 19.9914 Picture 5 - 12.9871 14.8561 15.8516 17.3258 20.5563 Picture 6 - 15.6243 15.9985 16.2546 18.2319 24.7963 Picture 7 - 11.8274 13.2873 14.7931 19.2291 19.9639 Picture 8 - 10.2034 11.2544 15.2698 16.3245 19.3312 Picture 9 - 14.5758 15.9152 18.3223 20.5513 24.3698 Picture10 - 11.4522 12.3756 13.4851 16.6334 19.3497 
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表 2 不同算法SSIM性能比较
Table 2 Performance comparison of different SSIM algorithms
SSIM Original Reference[3] Reference[7] Reference[13] Reference[16] Ours Picture 1 - 0.5609+ 0.5943 0.8321 0.8384 0.9611 Picture 2 - 0.5223 0.5081 0.6869 0.8612 0.8874 Picture 3 - 0.6186 0.7926 0.8031 0.8299 0.9212 Picture 4 - 0.5743 0.5178 0.8163 0.8752 0.9649 Picture 5 - 0.7121 0.7963 0.8263 0.8998 0.9088 Picture 6 - 0.6933 0.7432 0.7966 0.8364 0.8997 Picture 7 - 0.6074 0.5927 0.6988 0.7411 0.8796 Picture 8 - 0.5871 0.5988 0.6355 0.7843 0.8894 Picture 9 - 0.6121 0.6028 0.7123 0.7652 0.9126 Picture 10 - 0.5386 0.6103 0.7521 0.8419 0.8696
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表 3 不同算法UIQE性能比较
Table 3 UIQE performance comparison of different algorithms
UIQE Original Reference[3] Reference[7] Reference[13] Reference[16] Ours Picture 1 2.6449 4.9242 4.6436 5.0271 3.6167 5.2238 Picture 2 1.7252 4.0814 0.2696 3.9252 2.1831 4.9121 Picture 3 1.9542 3.0251 1.3447 3.5738 2.2406 4.4633 Picture 4 1.5241 4.5296 1.0328 4.2153 3.6574 5.9685 Picture 5 1.7551 3.1221 3.0217 3.9746 4.9962 6.2312 Picture 6 1.9978 2.2173 2.1179 4.5023 4.8785 6.0178 Picture 7 1.2212 1.3258 2.3647 4.2589 4.5565 6.9872 Picture 8 2.0121 2.2365 3.4562 4.2199 4.8456 5.5463 Picture 9 2.7853 2.8742 3.9893 4.5631 4.7987 6.2971 Picture 10 0.6721 2.9255 3.2372 3.4801 1.5899 3.6943
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