Polarization image fusion is the process of fusing an intensity image and a polarization parameter image solved by Stokes vector into a more detailed image. Conventional polarization image fusion strategies lack the targeting and robustness for fusing different targets in the images because they do not account for the differences in the characterization of the polarization properties of different materials, and the fusion rule is manually designed. Therefore, we propose a novel end-to-end network model called a semantic guided dual discriminator generative adversarial network (SGPF-GAN) to solve the polarization image fusion problem. We have specifically created a polarization image information quality discriminator (PIQD) block to guide the fusion process by employing this block in a weighted way. The network establishes an adversarial game relationship between a generator and two discriminators. The goal of the generator is to generate a fused image by weighted fusion of each semantic object of the image, the dual discriminator’s objective is to identify specific modalities (polarization/intensity) of various semantic targets. The results of qualitative and quantitative evaluations demonstrate the superiority of our SGPF-GAN in terms of visual effects and quantitative measures. Additionally, using this fusion approach to transparent, camouflaged hidden target detection and image segmentation can significantly boost the performance.
The color division of focal plane (DoFP) polarization sensor structure mostly uses Bayer filter and polarization filter superimposed on each other, which makes the polarization imaging unsatisfactory in terms of photon transmission rate and information fidelity. In order to obtain high-resolution polarization images and high-quality RGB images simultaneously, we simulate a sparse division of focal plane polarization sensor structure, and seek a sweet spot of the simultaneous distribution of the Bayer filter and the polarization filters to obtain both high-resolution polarization images and high-quality RGB images. In addition, From the perspective of sparse polarization sensor imaging, leaving aside the traditional idea of polarization intensity interpolation, we propose a new sparse Stokes vector completion method, in which the network structure avoids the introduction and amplification of noise during polarization information acquisition by mapping the S1 and S2 components directly. The sparsely polarimetric image demosaicing (Sparse-PDM) model is a progressive combined structure of RGB image artifact removal enhancement network and sparsely polarimetric image completion network, which aims to compensate sparsely polarimetric Stokes parameter images with the de-artifacts RGB image as a guide, thus achieving high-quality polarization information and RGB image acquisition. Qualitative and quantitative experimental results on both self-constructed and publicly available datasets prove the superiority of our method over state-of-the-art methods.
Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15 g/ml, 0.30 g/ml, and 0.50 g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10 cm, 20 cm, 30 cm, and 40 cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper’s algorithm is improved by about 69.26% over the original S0 image.
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