基于块排序的降噪方法及其在农业图像中的应用

Denoising Method and Application Based on Patch-ordering in Agricultural Image

农业图像采集过程中,环境因素常会带来噪声干扰,图像噪声又会对最终信息的分析结果带来影响,因此降噪对提高农业图像处理质量具有重要意义。基于块排序的非局部均值算法是一种有效的图像降噪方法,但是存在处理时间长,对大图像的处理内存要求高等问题。提出了分块优化方法,首先对大图像进行了适应于图像纹理丰富度的图像分块研究,然后分别对每个图像块进行处理。针对处理后的图像块再组合引起的边界效应,采用图像延拓的方法,有效地消除了边界影响,提高了图像降噪效果。实验结果表明,对于一般的硬件设备,改进的块排序非局部均值降噪算法能够快速处理农业中常用的图像。对于尺寸大小为512像素×512像素图像,当噪声标准偏差为50,分块数为16时,改进后的块排序降噪方法能够有效处理噪声图像。分块数为64时的处理速度是分块数为16时的1.89倍。

During the collection of agricultural images,noise often caused by environmental factors,and it often affects the final result of image processing.Thus,it is important to improve the quality of agricultural image.In recent years,the non-local means filter based on patch-ordering method has been applied to deal with Gaussian noise,which has obtained great success in denoising.However,the method suffers a shortcoming of long processing time and higher memory requirements,especially in large image processing.In order to improve the denoising effect,a block optimization algorithm was used in this paper.Firstly,the sampling image was split into several blocks,in which the number of the blocks was adapted to the image texture richness.After comparison with the speed of computer and the algorithm complexity,the segmented image blocks were obtained with an appropriate size to guarantee that they could be processed by the computer.Each image block was process separately.In view of the boundary effect caused by the combination of the processed image blocks,the method of image extension was applied to effectively eliminate the boundary influence and improve the image denoising effect.Experimental results show that,for general hardware devices,improved non-local means based on patchordering method could rapidly process the noise image commonly used in agriculture.For the size of the512 pixels × 512 pixels images,when the noise standard deviation was 50,the partition number was 16,the improved Non-local means based on patch-ordering method can effectively deal with the noise image,and the processing speed with 64 partitions was 1.89 times than 16 partitions.