Improved coded exposure for enhancing imaging quality and detection accuracy of moving targets

Baoqi Mao; Li Chen; Lin Han; Weimin Shen

doi:10.1117/12.2241613

27 September 2016 Improved coded exposure for enhancing imaging quality and detection accuracy of moving targets

Baoqi Mao, Li Chen, Lin Han, Weimin Shen

Proceedings Volume 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment; 968419 (2016) https://doi.org/10.1117/12.2241613
Event: Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), 2016, Suzhou, China

Abstract

The blur due to the rapidly relative motion between scene and camera during exposure has the well-known influence on the quality of acquired image and then target detection. An improved coded exposure is introduced in this paper to remove the image blur and obtain high quality image, so that the test accuracy of the surface defect and edge contour of motion objects can be enhanced. The improved exposure method takes advantage of code look-up table to control exposure process and image restoration. The restored images have higher Peak Signal-to-Noise Ratio (PSNR) and Structure SIMilarity (SSIM) than traditional deblur algorithm such as Wiener and regularization filter methods. The edge contour and defect of part samples, which move at constant speed relative to the industry camera used in our experiment, are detected with Sobel operator from the restored images. Experimental results verify that the improved coded exposure is better suitable for imaging moving object and detecting moving target than the traditional.

Citation Download Citation

Baoqi Mao, Li Chen, Lin Han, and Weimin Shen "Improved coded exposure for enhancing imaging quality and detection accuracy of moving targets", Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 968419 (27 September 2016); https://doi.org/10.1117/12.2241613

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