Research on High Reflective Imaging Technology Based on Compressed Sensing
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摘要:
高反光物体成像时反射的光强容易超出传感器接收光强的最大量化值，使得采集图像部分区域图像失真，严重影响信息传递。为了改善高反光成像饱和区域中数据丢失的状况，该文结合压缩感知这一新的采样理论提出基于压缩感知高反光成像方法，利用特定测量矩阵对目标图像进行线性采样，将CCD图像传感器的单个光强采样值与测量矩阵中的分布数据对应结合，对整合后的数据用算法进行恢复重建实现被测目标在高光环境中成像。以峰值信噪比和灰度直方图作为客观评定标准。实验表明，该成像方法鲁棒性较强、可行性较高，直方图检测饱和像素占比为0%，峰值信噪比为58.37 dB实现了在高光环境下不含饱和光成像，为压缩感知在成像应用中提供了新的方向。
Abstract:When imaging a highly reflective object, the light intensity reflected easily exceeds the maximum quantized value of the light intensity received by the sensor, which causes image distortion of the captured image in the saturated region of light intensity and seriously affects the quality of information transmission. In order to improve the data loss in the high-reflection imaging saturation region, a compression-sensing of high-reflection imaging method based on the new sampling theory of compressed sensing is proposed. A specific measurement matrix is used to conduct linear sampling of the target image, and the single light intensity sampling value of the CCD image sensor is combined with the distribution data in the measurement matrix, and the integrated data is restored and reconstructed with the algorithm to achieve the imaging of the measured target in the high-light environment. The peak signal to noise ratio and gray histogram are used as objective evaluation criteria. Experiments show that this imaging method is robust and feasible, with the proportion of saturated pixels in histogram detection 0% and the peak signal to noise ratio 58.37 dB, realizing the imaging without saturated light in the high-light environment, providing a new direction for the application of compressed sensing in imaging.
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Key words:
- High reflected light imaging /
- Compressed sensing /
- Data integration /
- Histogram
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表 1 不同采样率下两种恢复算法的MSE值与PSNR值
CS采样率 OMP算法 SAMP算法 MSE PSNR MSE PSNR 0.30 0.0158 66.1481 0.0166 65.9403 0.35 0.0150 66.3739 0.0154 66.2512 0.40 0.0143 66.5625 0.0142 66.6061 0.45 0.0139 66.7149 0.0135 66.8149 0.50 0.0136 66.7928 0.0133 66.9078 
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