Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.
.Automatic optical inspection technology (AOI) is a visual inspection technology that has developed rapidly in recent years. The high speed and accuracy of AOI can greatly enhance the efficiency of modern industrial production. However, when this technology is applied to optical components inspection, it encounters a challenge that the specular highlight induces over or under exposure during the imaging process, and further results in a low imaging contrast and unclear defect details of the targets. To solve this problem, a dark-field polarization imaging setup based on a division-of-focal-plane polarization camera was adopted to achieve high contrast defect images. Meanwhile, algorithms based on the improved LeNet-5 convolutional neural network were developed to recognize the defects. An accuracy above 99.5% was obtained for the distinction of defective and non-defective samples, and an accuracy of 94.4% was reached for the various defect classification. Our work demonstrated an effective application of polarization imaging and machine learning in AOI of optical components manufacturing.
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