为了提高光学联合变换相关识别的综合性能,提出一种基于SONG 相关的参考图像相移相位编码条纹调制畸变不变联合变换相关器。该相关器引入SONG 相关技术和SDF 方法,具有更加突出的抑制干扰信号能力、抗噪声性能和多目标识别能力,通过修正SDF 的约束条件,当输入图像存在多个畸变目标时,能够较好地实现相关输出的畸变不变性。仿真实验结果表明：当输入图像受到非覆盖性背景噪声影响时,该相关器在整个测试噪声强度区间内输出相关峰强度和峰噪比分别是引入SONG 相关技术前的1.87~5.16 倍和1.88~5.19 倍,其平均偏差量能够基本维持在0.04 以下;当输入图像受到替换噪声影响时,该相关器在大部分测试噪声强度区间内都能给出较高的相关峰强度和峰噪比,在噪声强度不高于0.4 时,其平均偏差量能够低于0.1。

In order to get good general performance of optical joint transform correlation pattern recognition, a new reference shifted phase-encoded fringe-adjusted joint transform correlator for distortion invariant pattern recognition is proposed based on Sliced Orthogonal Nonlinear Generalized (SONG) correlation. The new correlator not only enhances the performance of resisting disturbance signal and noise, but also has a good performance of multiple target recognition by introducing the technique of SONG correlation and Synthetic Discriminant Function (SDF). It realizes distortion invariant pattern recognition by modifying the constraint condition of SDF when several distorted targets occur in the input image. The results of simulation indicate that correlation peak value and peak-to-noise ratio of output is 1.87~5.16 times and 1.88~5.19 times of those without introduction of SONG correlation respectively during whole noise density intervals when input image is destroyed by nonoverlapping background noise. And average deviation is less than 0.04. Preferable correlation peak value and peak-to-noise ratio of output can be obtained during most of noise density intervals when input image is destroyed by substitutive noise, and average deviation is less than 0.1 when noise density is not more than 0.4.