This paper presents a joint dehazing and denoising scheme for an image taken in hazy conditions. Conventional image dehazing methods may amplify the noise depending on the distance and density of the haze. To suppress the noise and improve the dehazing performance, an imaging model is modified by adding the process of amplifying the noise in hazy conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for dehazing the image. The proposed iterative image dehazing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and dehazed image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different days are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme weather conditions without amplifying the noise.

Multiband polarization imaging is an emerging sensing method that enables simultaneous acquisition of multiband spectral and multiangle polarization information of an object of interest in the scene. Spectral signatures of the light reflected from a target reveal the characteristics of the material composing the target while polarized light provides useful information on the surface features such as light scattering and specular reflection. In multiband spectral imaging, combined spectral and polarization information offers a comprehensive representation of an object utilizing complementary spectral and polarization information in visual sensing. Multiband polarization imaging has demonstrated a potential in the recognition of targets in challenging operating environments such as low-contrast and hazy conditions. This paper presents the concept and recent advances of multiband polarization imaging techniques, in particular, a bioinspired multiband polarization vision system. Applications of multiband polarization imaging in various fields include atmospheric observation, object detection and classification, medical diagnostics, surveillance, and 3D object reconstruction.

Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.

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