# Contrast Sensitivity Based Multiscale Base-Detail Separation for Enhanced HDR Imaging
## 5 Simulations  
In this section, we experimented with the rendering performance of the tone mapping method with the proposed CSF filter. In the experiments, we considered that the resolution of the experimental images was 30 pixels/degree (maximum range for spatial frequency) for the row and the column. Test images included the Macbeth color checker, and bright and noisy dark regions. As the viewer may feel less contrast sensitivity under the dim surround, we used test images of night indoor views. Figure 6 shows the HDR images rendered by comparable methods for evaluating the detail rendering performance. Figure 5(a) shows the rendered HDR images by the default Stevens adjust for detail layers in the iCAM06. Figure 5(b) shows rendered images with additional detail enhancement using the peak ratio of CSFs between bright and dim surrounds, as well as the Stevens adjust. The rendered images from the proposed model are shown in Figure 5(c). It is easily confirmed that the proposed model is more sensitive than those of different methods around the edge and the object boundary. The detail enhancement is well-compared in color patches and in the background graph in first row images of Figure 6. Furthermore, to confirm contrast enhancement, we focused on the region around the lights. In the abrupt luminance change, such as object and light edges, the proposed method shows more clear edge lines when compared to the other methods. Figure 5: Flowchart of proposed segmented base–detail separation. Moreover, global enhancement using a CSF gain causes the color overshoots as shown in the abrupt edges of first and second row in Figure 6b. In addition, the sharpness enhancement and noise reduction compared to the result of global enhancement is shown in the upper and lower parts around the table in third row images of Figure 6. In order to compare the detail results, the edge renderings and noise differences in the detail area of the RGB channels were compared in Figure 7 for the scan line area of the bright and dark regions in the third and fourth rows in Figure 6b,c. In Figure 7a,  
Figure 6: Comparison of detail rendering performance for each method. High dynamic range (HDR) rendered image by (**a**) Stevens adjustment, (**b**) Stevens adjustment with the global CSF gain and (**c**) the proposed method. the proposed method shows that the local contrast is equal to or better than the global method for the bright area, and simultaneously that the dark noise was reduced by far in the dark area in Figure 7b. In the proposed method of Figure 7c, the drastic change of intensity occurs in the transition region between background and object. As a result, the detail of the image is improved and the blur and noise are reduced. We compare the proposed tone mapping method with the different methods for 15 test images. These tone mapping methods include a tone reproduction model considering lightness perception with characterizing the viewing environment and display, [PERSON] (2012) [PERSON] (2012), hybrid L1-L0 layer decomposition model, [PERSON] (2018) [PERSON] (2018), iCAM06 and the proposed method. User parameters of the simulated method are set as follows:  
1. [PERSON] (2012) Parameters are same as the environmental parameters of iCAM06 and the adapting luminance is 20 % of maximum luminance,
2. [PERSON] (2018) B1 layer smoothness degree: \(\lambda_{1}=0.3\) Detail layer smoothness degree: \(\lambda_{2}=\lambda_{1}\times 0.1\) B2 layer smoothness degree: \(\lambda_{3}=0.1\) Gamma value: \(\gamma=2\),
3. iCAM06 maximum luminance: \(L_{max}=500\) Overall contrast: \(p=0.75\) Surround adjustment: gamma value = 1  
Figure 7: Comparison of the RGB channel’s noise for the scan line positions of Figure 6. (**a**) bright region (monitor) in third row, (**b**) dark region (cup) in third row, and (**c**) dark region (desktop case) in fourth row.