无规律运动条件下立木模糊图像恢复方法研究
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摘要
林业机器人在复杂的林区环境作业时,在松软或者潮湿林地上行走容易发生滑动,这些无规律运动使林业机器人视觉系统获取的图像产生运动模糊,从而影响其作业环境识别和环境建模的准确性；此外,一些大型林业机器人在作业时也经常发生无规律振动或抖动,使其计算机视觉系统获取的图像产生运动模糊。鉴于此,本文作者认为有必要专门研究林业机器人无规律运动条件下的立木运动模糊图像恢复方法。
本文主要研究无规律运动条件下立木运动模糊图像恢复方法,研究内容和研究结果如下：
1.针对立木运动模糊图像的去噪问题,提出了基于鲁棒联合稀疏编码(RSSC)去噪算法。联合稀疏正则项可有效利用图像块的相似性,同时元素稀疏正则项考虑了图像块之间的差别,从而提高去噪效果。通过实验与K-SVD和BM3D算法进行对比,结果表明,在高斯噪声强度6n=15,(?)n=25,(?)n=35的不同情况下,RSSC算法的SSIM值均要高于K-SVD和BM3D算法。
2.针对匀速直线运动模糊图像恢复方法中运动模糊角度鉴别耗时长、计算量大的缺点,提出一种改进的运动模糊角度自动鉴别方法。对目标图像进行切分,由大到小改变步长鉴别运动模糊角度,经过多次方向辨别后,去除差异较大的角度,取平均值得出运动模糊角度。通过实验与传统方法比较,本文提出的方法既减少了计算时间,提高了计算效率,精确度上也优于传统方法。
3.提出了基于字典迁移的稀疏正则化运动模糊图像恢复方法。该方法从需要恢复的图像中采集一些图像块作为训练样本,将全局训练的字典迁移到特定的需要去运动模糊的图像上,在学习一组自适应字典的同时又提高了准确率。采用运动模糊核、高斯模糊核、均匀模糊核与Krishnan和Dilip算法进行实验对比,本文提出的方法SSIM值均优于其它两种方法,恢复的图像更接近原始图像,解决了“振铃”现象。
4.研究了基于小波框架的运动模糊图像恢复方法,提出了新数学模型。在新数学模型的初始模糊核中引入了两个正则项,一项是减少大量的小框架系数,可以看作是减少大量离散像素；另一项是通过控制剩余极小元偏向较大连接支撑补偿‖Wp‖1所造成的误差。通过平衡调节这两个正则项得到合理的运动模糊核,然后用Bregman法恢复出原始图像。对匀速直线运动、匀速曲线运动和无规律运动的模糊图像进行实验,并与Shan et al.和Fergus et al.方法进行了数据对比,实验证明,本文提出的方法对无规律运动模糊图像的恢复效果最好,SSIM值最高。此外分析了本文方法的抗噪能力,结果表明本文方法抗椒盐噪声能力很强。
When robot is walking on the soft or moist forest floor on the complex forestry region, it is prone for it to slip and slide. The slip and slide without any law could cause motion blurred images to the forestry robot vision system. Thus it affects the accuracy of working environment recognition and environment modeling. When some large forestry robots are working, irregular trembles and vibrates often occur, which generates the motion blur to the images from vision system of forestry robot. So it is necessary to research on the restoration methods about motion blurred images of standing trees in the case of random motion for the forestry robot.
Domestic and foreign scholars study extensively on the motion blurred image restoration methods at present, but motion blurred image restoration problem in special natural environment is rarely reported to the forestry robot. The restoration method of motion blurred image of standing trees to forestry robot with random motion is researched in this paper. The main contents and innovation as follows:
1. A de-noising algorithm based on robust joint sparse coding (RSSC) is proposed for the de-noising problem to motion blurred image of standing trees. Image block similarity is effectively utilized by joint sparse regularization, the difference between the image block is in the view of elements sparse regularization, which could improve the effect of de-noising. Comparing with K-SVD and BM3D algorithm, the experiment shows that the SSIM value of RSSC algorithm is higher than K-SVD and BM3D algorithm with different Gaussian noise (?)n=15,25,35.
2. In view of the defects of long time calculating and large computation for motion blurred angle identification in restoration method of uniform linear motion blurred image, an automatic identification method of improved motion blurred angle is proposed. Making segmentation of the target image and changing the step as descending to identify the motion blur angle, removing the angle with large differences, then the motion blurred angle will be get after taking the average value. By comparing with the traditional method in the experiment, the method in this paper can reduce the calculation time and improve the computational efficiency, its accuracy is better than traditional methods.
3. A sparse regularization motion blurred image restoration method based on dictionary migration is proposed in this paper. This method take the image block collected from the image that need to be restored as a training sample, and it takes the global trained dictionary to blurred images which are special and need to de-motion, this method studies a set of adaptive learning dictionary while improving the accuracy. Comparison is adopted among motion blur kernel, Gaussian blur kernel, uniform blur kernel and Krishnan&Dilip algorithms in the experiment, the SSIM value in this paper is better than the other two algorithms, the restored image is much more close to the original image and it solves the ringing effects.
4. Motion image restoration method based on wavelet frame is studied and a new mathematical model is proposed. In the new model, two regularization items are introduced by given initial blur kernel; one is to reduce the large number of wavelet frame coefficients which can be seen as reducing the number of discrete pixels. The other is to compensate the error caused by‖Wp‖1through controlling the remaining minimizer to deviate to the larger connection support. By adjusting the balance of the two regularization terms, the method gets the reasonable motion blur kernel and restores the original image with Bregman method. Blur image experiments are taken with uniform linear motion, uniform curve motion and erratic motion, comparing the data with the methods of Shan et al. and Fergus et al., the restored effect for irregular motion blur image in this paper is the best among the three methods, and the SSIM is the highest. The noise immunity is analyzed in this paper. The results show that the method in this paper has a strong resistance to salt and pepper noise.
本文主要研究无规律运动条件下立木运动模糊图像恢复方法,研究内容和研究结果如下：
1.针对立木运动模糊图像的去噪问题,提出了基于鲁棒联合稀疏编码(RSSC)去噪算法。联合稀疏正则项可有效利用图像块的相似性,同时元素稀疏正则项考虑了图像块之间的差别,从而提高去噪效果。通过实验与K-SVD和BM3D算法进行对比,结果表明,在高斯噪声强度6n=15,(?)n=25,(?)n=35的不同情况下,RSSC算法的SSIM值均要高于K-SVD和BM3D算法。
2.针对匀速直线运动模糊图像恢复方法中运动模糊角度鉴别耗时长、计算量大的缺点,提出一种改进的运动模糊角度自动鉴别方法。对目标图像进行切分,由大到小改变步长鉴别运动模糊角度,经过多次方向辨别后,去除差异较大的角度,取平均值得出运动模糊角度。通过实验与传统方法比较,本文提出的方法既减少了计算时间,提高了计算效率,精确度上也优于传统方法。
3.提出了基于字典迁移的稀疏正则化运动模糊图像恢复方法。该方法从需要恢复的图像中采集一些图像块作为训练样本,将全局训练的字典迁移到特定的需要去运动模糊的图像上,在学习一组自适应字典的同时又提高了准确率。采用运动模糊核、高斯模糊核、均匀模糊核与Krishnan和Dilip算法进行实验对比,本文提出的方法SSIM值均优于其它两种方法,恢复的图像更接近原始图像,解决了“振铃”现象。
4.研究了基于小波框架的运动模糊图像恢复方法,提出了新数学模型。在新数学模型的初始模糊核中引入了两个正则项,一项是减少大量的小框架系数,可以看作是减少大量离散像素；另一项是通过控制剩余极小元偏向较大连接支撑补偿‖Wp‖1所造成的误差。通过平衡调节这两个正则项得到合理的运动模糊核,然后用Bregman法恢复出原始图像。对匀速直线运动、匀速曲线运动和无规律运动的模糊图像进行实验,并与Shan et al.和Fergus et al.方法进行了数据对比,实验证明,本文提出的方法对无规律运动模糊图像的恢复效果最好,SSIM值最高。此外分析了本文方法的抗噪能力,结果表明本文方法抗椒盐噪声能力很强。
When robot is walking on the soft or moist forest floor on the complex forestry region, it is prone for it to slip and slide. The slip and slide without any law could cause motion blurred images to the forestry robot vision system. Thus it affects the accuracy of working environment recognition and environment modeling. When some large forestry robots are working, irregular trembles and vibrates often occur, which generates the motion blur to the images from vision system of forestry robot. So it is necessary to research on the restoration methods about motion blurred images of standing trees in the case of random motion for the forestry robot.
Domestic and foreign scholars study extensively on the motion blurred image restoration methods at present, but motion blurred image restoration problem in special natural environment is rarely reported to the forestry robot. The restoration method of motion blurred image of standing trees to forestry robot with random motion is researched in this paper. The main contents and innovation as follows:
1. A de-noising algorithm based on robust joint sparse coding (RSSC) is proposed for the de-noising problem to motion blurred image of standing trees. Image block similarity is effectively utilized by joint sparse regularization, the difference between the image block is in the view of elements sparse regularization, which could improve the effect of de-noising. Comparing with K-SVD and BM3D algorithm, the experiment shows that the SSIM value of RSSC algorithm is higher than K-SVD and BM3D algorithm with different Gaussian noise (?)n=15,25,35.
2. In view of the defects of long time calculating and large computation for motion blurred angle identification in restoration method of uniform linear motion blurred image, an automatic identification method of improved motion blurred angle is proposed. Making segmentation of the target image and changing the step as descending to identify the motion blur angle, removing the angle with large differences, then the motion blurred angle will be get after taking the average value. By comparing with the traditional method in the experiment, the method in this paper can reduce the calculation time and improve the computational efficiency, its accuracy is better than traditional methods.
3. A sparse regularization motion blurred image restoration method based on dictionary migration is proposed in this paper. This method take the image block collected from the image that need to be restored as a training sample, and it takes the global trained dictionary to blurred images which are special and need to de-motion, this method studies a set of adaptive learning dictionary while improving the accuracy. Comparison is adopted among motion blur kernel, Gaussian blur kernel, uniform blur kernel and Krishnan&Dilip algorithms in the experiment, the SSIM value in this paper is better than the other two algorithms, the restored image is much more close to the original image and it solves the ringing effects.
4. Motion image restoration method based on wavelet frame is studied and a new mathematical model is proposed. In the new model, two regularization items are introduced by given initial blur kernel; one is to reduce the large number of wavelet frame coefficients which can be seen as reducing the number of discrete pixels. The other is to compensate the error caused by‖Wp‖1through controlling the remaining minimizer to deviate to the larger connection support. By adjusting the balance of the two regularization terms, the method gets the reasonable motion blur kernel and restores the original image with Bregman method. Blur image experiments are taken with uniform linear motion, uniform curve motion and erratic motion, comparing the data with the methods of Shan et al. and Fergus et al., the restored effect for irregular motion blur image in this paper is the best among the three methods, and the SSIM is the highest. The noise immunity is analyzed in this paper. The results show that the method in this paper has a strong resistance to salt and pepper noise.
引文
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