# Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions
## 2 Results and Discussion
The fusion image \(I_{IF}\) (Figure 2c) was dynamically reconstructed by selecting the pixel value at the image index where the pixel-weight was the minimum on a per-pixel basis. With lighting obstructions removed, the fusion image revealed hidden features of the vehicle, windshield, and crosswalk (Figure 2c). To quantitatively assess the fusion process, we constructed synthetic datasets recapitulating experiments in which lighting obstructions were added to ground truth images. As different locations within images may have different optimal polarizations as observed in experiments, we compared image quality of fusion images as a function of number of optimal polarizations present (unimodal, bimodal, trimodal). We quantitatively scored image quality using quantitative metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean squared error (RMSE). As the number of optimal polarizations present increased, fusion images showed significantly enhanced PSNR as compared to reconstructions identifying only a single optimal polarization (Figure S3). Fusion images reduced the mean squared error by two orders of magnitude on average, resulting in a PSNR increased by 20 with nearly zero variance (Figure 2d). Fusion images demonstrated that SSIM values on average exceeded 0.95 (Figure 2e) and the RMSE on average reduced by a factor of 5 (Figure 2f). These results provide evidence that fusion images dynamically generated by the IF algorithm significantly enhanced image quality. Having constructed fusion images in the modeled environment, we proceeded to the outdoor environment (Figure 3a). Many previous methods commonly address specific lighting obstructions, requiring specific parameters of the illumination and vision system. In this work, we demonstrated IF was robust for various lighting obstructions, enabling classification. In outdoor scenes, we observed various lighting obstructions. In Figure 3a(i), specular reflections added unsaturated features (trees) to the scene of buildings with glass walls, occluding hidden objects. We used IF to efficiently select optimal polarizations, dynamically reconstruct a fusion image, and classify and remove unsaturated features. With unsaturated lighting obstructions removed, the fusion image revealed hidden objects (curtains) in the scene (Figure 3a(ii)). IF was also robust for saturated lighting obstructions (Figure 3b). In Figure 3b(i), specular reflections added saturated features (sun) to the scene and occluded hidden objects. Using IF to efficiently determine optimal polarizations and dynamically construct a fusion image, we classified and removed saturated features. The fusion image in Figure 3b(ii) revealed hidden objects (blinds) in the scene. As specular reflections can be also classified as polarized or partially polarized, we quantitatively characterized IF using synthetic datasets in polarized and partially polarized scenarios(Figure 3c). We quantitatively scored fusion image quality using PSNR, SSIM and RMSE. For polarized and partially polarized lighting obstructions, fusion images showed an increase in PSNR (Figure 3d) and SSIM (Figure 3e). Fusion images displayed a decrease in RMSE (Figure 3f) for polarized and partially polarized lighting obstructions. While image quality was higher for polarized lighting obstructions, these results support that IF also improves image quality for partially polarized lighting obstructions. As specular reflections are usually at least partially polarized, IF can be used to classify and remove various lighting obstructions. Finally, we investigated the real-time performance of IF. In addition to efficiently determining optimal polarizations, a downsampling process was also implemented to further decrease memory and computational complexity of IF (Figure 4a). In outdoor scenes, lighting obstructions were observed, and the Image domain focused IF algorithm (Figure S4, Methods) was used to determine the optimal polarizations.