# Retinex-Based Relighting for Night Photography
## 4 Experimental Results
### Visual Comparison  
In this section, we visually compare each method. First, we compared with a light that spreads omnidirectionally from a point source using four different filters: GF (Figure 13a), JBF (Figure 13b), DTF (Figure 13c), and AMF (Figure 13d). Each filter diffuses a light source, and JBF, DTF, and AMF use the input image as a guided image. GF diffused regardless of the object boundary and even illuminated dark areas (Figure 13e). On the other hand, edge-preserving filters had weights depending on the difference in the guide image to prevent color leaking between different objects. JBF is characterized as being able to spread object awareness over a wide area (Figure 13f). In addition, JBF can also be spread out similar to GF regardless of the object by making the range smoothing parameter \(\sigma_{r}\) infinitely large. DTF and AMF are characterized to illuminate the light more locally (Figure 13b,g,h). Each edge-preserving filter, JBF, DTF, and AMF, suppresses subnormal numbers to accelerate their filtering speeds [61], and the slower method of JBF is also accelerated by vectorization [62].  
Next, we compared the directional light generation algorithms with the PSFs of GF and JBF using the SSR reflectance and the proposed reflectance. Figure 14 shows the diffusing lights and the rendering results. With the SSR reflectance, the sampling points tend to be located outside of the subject; thus, light leaks out of the object. The JBF PSF can reduce leakage to some extent, but it is challenging when completely outside the points. With the proposed reflectance, the sampling points concentrate on the subject, and the JBF PSF further concentrates the light within the subject. The other rendering results by the proposed reflectance, and JBF with changing color are shown in Figure 15 (omnidirectional) and Figure 16 (directional). The expanded images of Figures 15 and 16 are shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Image** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **h** & **Ave.** \\ \hline Random & 0.8609 & 0.7173 & 0.9084 & 0.8896 & 0.8163 & 0.8566 & 0.8637 & 0.7697 & 0.8353 \\ URetiinex-Net [59] & 0.5159 & 0.4322 & 0.8307 & 0.5318 & 0.5179 & 0.6167 & 0.6596 & 0.3889 & 0.5617 \\ LR3M [6] & 0.7539 & 0.488 & 0.8846 & 0.8482 & 0.6364 & 0.8077 & 0.8114 & 0.5941 & 0.7280 \\ Saliency (OpenCV) & 0.3831 & 0.4790 & 0.5980 & 0.2976 & 0.5483 & 0.5108 & 0.4966 & 0.5993 & 0.4891 \\ SaliencyELD [60] & 0.4230 & **0.2832** & 0.4301 & 0.1945 & 0.3101 & 0.4858 & 0.3074 & 0.3216 & 0.3445 \\ Proposed & **0.1618** & 0.3275 & **0.3727** & **0.1364** & **0.2494** & **0.2159** & **0.2556** & **0.2842** & **0.2504** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Probability that the points are out of the object (8-bit luminance value threshold: 50). Probability of random sampling points is the percentage above or below the threshold. The other probabilities are the percentage of cases where the dithering points of the input image are above or below the threshold. The top score for each image is shown in boldface.  
Figure 13: Comparison of 4 omnidirectional diffusion methods. (**Top**) PSFs; (**Bottom**) the output image with diffusion.  
Figure 16: Rendering results with directional diffusion (JBF).  
Figure 17: Cont.  
Figure 14: Directional diffusing with 5 points selected: (**a,b,e,f**) from SSR, (**c,d,g,h**) from the proposed.  
Figure 15: Rendering results with omnidirectional diffusion (JBF).