Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment.

Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.

Polarization image demosaicing and RGB image enhancement for a color polarization sparse focal plane array.

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.

CenterPNets: A Multi-Task Shared Network for Traffic Perception.

The importance of panoramic traffic perception tasks in autonomous driving is increasing, so shared networks with high accuracy are becoming increasingly important. In this paper, we propose a multi-task shared sensing network, called CenterPNets, that can perform the three major detection tasks of target detection, driving area segmentation, and lane detection in traffic sensing in one go and propose several key optimizations to improve the overall detection performance. First, this paper proposes an efficient detection head and segmentation head based on a shared path aggregation network to improve the overall reuse rate of CenterPNets and an efficient multi-task joint training loss function to optimize the model. Secondly, the detection head branch uses an anchor-free frame mechanism to automatically regress target location information to improve the inference speed of the model. Finally, the split-head branch fuses deep multi-scale features with shallow fine-grained features, ensuring that the extracted features are rich in detail. CenterPNets achieves an average detection accuracy of 75.8% on the publicly available large-scale Berkeley DeepDrive dataset, with an intersection ratio of 92.8% and 32.1% for driveableareas and lane areas, respectively. Therefore, CenterPNets is a precise and effective solution to the multi-tasking detection issue.

Semantic-guided polarization image fusion method based on a dual-discriminator GAN.

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.

Low SNR difference Nyquist-WDM channels with optical sinc-shaped pulses based on flat electro-optic frequency combs.

We experimentally demonstrate Nyquist wavelength-division-multiplexed (WDM) channels with a low signal-to-noise ratio (SNR) difference based on flat electro-optic combs (EOCs), which reduce the interchannel crosstalk penalty in Nyquist-WDM transmission with no guard band. The five Nyquist-WDM channels are generated through the insertion of uniform and coherent lines around each line of the EOCs from a dual-parallel Mach-Zehnder modulator. For the five channels, the normalized root-mean-square error of optical sinc-shaped pulses at a repetition rate of 9 GHz is between 1.23% and 2.04%. The SNRs of the Nyquist signal can be better than 30 dB by using flat EOCs with a narrow linewidth as WDM sources, and the difference in SNR is less than 0.6 dB for the WDM channels. The transmission performance of five Nyquist-WDM channels with no guard band is compared in a 56 km fiber link. The results show that our scheme provides a minimum interchannel sensitivity penalty of 0.7 dB at the forward-error-correction limit. The Nyquist-WDM channels with low SNR difference can effectively improve the communication performance of the Nyquist-WDM system.