# Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions
## 2 Results and Discussion  
We firstly incorporated the iNC (integrated nanoscopic correction) [21; 22], developed previously for nanoscopic imaging [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], into a vision system (CMOS camera) to address challenging and changing lighting conditions. The iNC was comprised of a series of fixed and variable retarders for systematic voltage control and dynamic modulation of the transmission polarization. By modulating the voltage to the iNC from 3 V to 10 V, the output transmission intensity corresponded to different input polarizations between 0\({}^{\circ}\) to 180\({}^{\circ}\) (Figure S1). For real-time capabilities, the iNC operated with millisecond response time (40 ms) (Figure S2). In contrast to conventional approaches using predetermined polarizations [9; 12; 20], the intelligent real-time fusion algorithm dynamically modulated the iNC to determine the optimal polarization to physically remove lighting obstructions using iterative feedback (Methods). We segmented images based on polarization and analyzed each pixel such that the relation of the intensity of the pixel to the segmentation was not in the spatial domain but in the polarization domain. Pixel-based weights were then assigned in the intelligent polarization selection process where each pixel was evaluated. Finally, a new fusion image was generated from pixel-based weights selected at all pixels (Figure 2a). In this way, the iNC enabled intelligent polarization selection as compared to conventional predetermination of polarization. Figure 2: Fusion images by intelligent polarization selection uncovered subtle features of occluded objects. (**a**) Conceptual schematic of intelligent polarization selection and fusion imaging process. Intelligent real-time fusion (IF) algorithm determined optimal polarizations using iterative feedback. The fusion image was reconstructed by selecting the pixel value at the image index where the pixel-weightwas the minimum on a per-pixel basis. I\({}_{\text{IF}}\) was free of lighting obstructions. (**b**) Model vehicle photograph overlaid with intensity map at optimal polarization \(\theta_{1}=125^{\circ}\) and optimal polarization \(\theta_{2}=45^{\circ}\) determined by the IF algorithm. Scale bar: 1.3 cm. (**c**) Model vehicle photograph overlaid with intensity map for \(I_{0}\) and \(I_{IF}\). Scale bar: 1.3 cm. (**d**) Graph peak signal-to-noise ratio (PSNR) for \(I_{0}\) (red color) and \(I_{IF}\) (blue color) comparing number of optimal polarizations present: unimodal, bimodal, trimodal. (**e**) Graph structural similarity (SSIM) for \(I_{0}\) (red color) and \(I_{IF}\) (blue color) comparing number of optimal polarizations present: unimodal, bimodal, trimodal. (**f**) Graph root mean squared error (RSME) for \(I_{0}\) (red color) and \(I_{IF}\) (blue color) comparing number of optimal polarizations present: unimodal, bimodal, trimodal. In (**d**-**f**), ten datasets were used. Each dataset consisted of 9 images captured at different polarizations with \(\theta_{resol}=20^{\circ}\). Each datapoint was calculated by averaging 9 images for \(I_{0}\) (red color). Each datapoint was a fusion image for \(I_{IF}\) (blue color). To experimentally characterize lighting in a controlled manner, we firstly created a modeled environment consisting of a model vehicle and crosswalk (Figure 2b,c). The controlled environment facilitated systematic creation, modification and reproducibility of lighting conditions. Illuminating the modeled scene showed image \(I_{0}\) (Figure 2c) in which different locations within the image had different optimal polarizations. To efficiently determine optimal polarizations, we used the intelligent real-time fusion (IF) algorithm to acquire images using iterative feedback. At one optimal polarization \(\theta_{1}\), lighting obstructions were visible on the crosswalk and vehicle windshield (Figure 2b). At a different optimal polarization \(\theta_{2}\), lighting obstructions were present on the side of the vehicle.