摘要
由于背景噪声及探测器噪声的存在,夏克-哈特曼波前传感器质心探测误差较大,本文提出了一种在稀疏域去除背景噪声及探测器噪声的方法。首先采用二维高斯模型生成光斑信号样本图像,构造超完备目标字典,依次对测试图像分块计算其在超完备字典中的表示系数,利用噪声、背景和目标表示系数的不同,通过认为设定阈值来判别该图像块是否为光斑信号。结果表明,本文处理方法能够较好的提取出光斑信号,且与减阈值算法相比,其处理后质心偏差、RMS及PV值都较小。
Due to the background noise and the detector noise,the centroid error of Shack-Hartmann wavefront sensor is relatively large.A method to filter the noise in sparse region is proposed.First,the sample image is generated by using the two-dimension Gauss model and the over-complete target dictionary is constructed.Then,the sub-image blocks of the test image are extracted successively and the corresponding coefficients are calculated with the constructed over-complete target dictionary.Since the coefficients between the noise and the target are large different,the difference by setting a threshold is used to subtract the target.Experimental results show that,the target can be well subtracted,while the centroid deviation and the RMS and PV of the centroid are smaller than using the method of subtracting threshold.
出处
《光电工程》
CAS
CSCD
北大核心
2016年第4期48-54,共7页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(61071164)
江苏省高校基金重大项目(12KJA510001)
江苏省气象探测与信息处理重点实验室项目(KDXS1405)
江苏省2015大学生实践创新计划(201510300249)
江苏高校优势学科Ⅱ期建设工程资助项目
关键词
自适应光学
夏克-哈特曼波前传感器
稀疏表示
质心偏差
adaptive optics
Shack-Hartmann wavefront sensor
sparse representation
centroid deviation