# Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions
## 2 Results and Discussion
During the int  
Figure 3: IF algorithm was robust for various lighting obstructions enabling classification. (**a**) Unsaturated lighting obstructions: Photograph of buildings with glass walls overlaid with intensity map for (**i**) \(I_{0}\) and (**ii**) \(I_{\text{IF}}\). Scale bar: 1 ft. (**b**) Saturated lighting obstructions: Photograph of buildings with glass walls overlaid with intensity map for (**i**) \(I_{0}\) and (**ii**) \(I_{\text{IF}}\). Scale bar: 1 ft. (**c**(**i**)) Polarized lighting obstructions: Conceptual schematic showing reconstructed intelligent fusion image \(I_{\text{IF}}\). (**c**(**ii**)) Partially polarized lighting obstructions: Conceptual schematic showing IF reconstructed intelligent fusion image \(I_{\text{IF}}\). (**d**) Graph of PSNR for \(I_{0}\) (red color) and \(I_{\text{IF}}\) (blue color) comparing polarized versus partially polarized light obstructions. (**e**) Graph of SSIM for \(I_{0}\) (red color) and \(I_{\text{IF}}\) (blue color) comparing polarized versus partially polarized light obstructions. (**f**) Graph of RSME for \(I_{0}\) (red color) and \(I_{\text{IF}}\) (blue color) comparing polarized versus partially polarized light obstructions. 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 \(I_{\text{IF}}\) (blue color). Multiple optimal polarizations were present (bimodal). tion process, the input dimensions (3036 \(\times\) 4024 pixels) of images were reduced to decrease memory and computational complexity for the purpose of capturing dynamic scenes at high speeds. To systematically study time performance, we varied input dimensions as a function of number of optimal polarizations present (Figure 4b-d). We observed an exponential relationship between the process time and the input dimensions, where the time for processing the set of images was approximately 1.03 s for downsampling factor of 16 (Figure 4b(i),c(i),d(i)). As a quantitative metric of image quality, the PSNR of the fusion images were calculated at different downsampling factors. (Figure 4b(ii),c(ii),d(ii)). We found that the change in PSNR with various downsampling factor was marginal. This finding is significant because the time cost can be reduced without compromising image quality. While the change in PSNR with various downsampling factor was marginal, increasing the downsampling factor indefinitely is such that lighting obstructions can no longer be distinguished. Thus, we considered a response time of 1.03 s (downsampling factor of 16) as the threshold for real-time and to maintain highest possible image quality and accuracy of the algorithm. For applications where millisecond high speed is a priority, the processing speed can be further reduced to milliseconds by further increasing the downsampling factor (response time 450 ms; downsampling factor 32). Figure 4: IF algorithm increased time performance without compromising image quality. (**a**) Conceptual schematic of the downsampling process in the IF algorithm. During the intelligent polarization selection process, the input dimensions of images were reduced. IF determined optimal polarizations using iterative feedback. The fusion image was reconstructed by selecting the pixel value at the image index where the pixel-weight was the minimum on a per-pixel basis. \(I_{\text{IF}}\) was free of lighting obstructions. (**b**) Single optimal polarization present (unimodal): (**i**) Graph of time versus downsampling factor (red, green, blue, grey colors). (**ii**) Graph of PSNR versus downsampling factor (red, green, blue, grey colors). (**c**) Multiple optimal polarizations present (bimodal): (**i**) Graph of time versus downsampling factor (red, green, blue, grey colors). (**ii**) Graph of PSNR versus downsampling factor (red, green, blue, grey colors).