## 4 Results
In addition, we adopted the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) [25] as performance metrics for quantitative comparison. Table 2 shows the evaluation results of the original images and the resulting images of the two stages. Both PSNR and SSIM evaluation metrics were significantly improved in the first stage. The results showed that the proposed approach can effectively learn the intensity distribution of the HDR and redistribute image intensity appropriately. In stage 2, through pixel-by-pixel adjustments based on reinforcement learning, PSNR and SSIM were further improved by 0.273 and 0.013, respectively. Figure 6: The resulting images of the proposed approach. The left column shows LDR images, the second column shows the resulting 12-bit HDR images of stage 1, the third column shows the resulting 16-bit HDR images of stage 2, and the right column illustrates the ground-truth HDR images. From the results, the proposed approach can reconstruct the desirable HDR images with high quality. We selected four images from the HDR-EYE dataset for illustration. Figure 7 shows the input LDR images in the left column, our output HDR results in the middle column, and ground-truth HDR images in the right column. Images were converted to grayscale representations to better demonstrate contrast enhancement. As shown in the left column of Figure 7, the original images were partially underexposed, resulting in the loss of detailed textures. For example, the tree trunks in the first image, the stairs in the second image, building shadows in the third image, and the shadows under the parasols in the fourth image. All these difficult-to-identify details were greatly improved by the proposed technique, as shown in the middle column images of Figure 7. By redistributing pixel intensities based on predicted HDR histograms, pixels that were originally accumulated in dark areas due to underexposure can be more reasonably allocated. In the first result, the textures of the tree trunks became clearly visible and the changes in light and shadow on the building became more vivid. The second and third results were also improved for underexposure of stairs and building shadows. In the final result, the textural details of the building were clearly visible in the shadows and the seating areas under the parasols were no longer dark. Figure 7: The resulting images are compared with the input LDR images and ground truth HDR images in tone-mapped grayscale representations. The left column shows LDR images, the middle column shows our output HDR images, and the right column illustrates the ground-truth HDR images. In the last experiment, we compared the proposed approach with previous CNN-based methods, including HDRCNN [10], DrTMO [8], ExpandNet [14], and SingleHDR [11]. For existing works, pre-trained weights provided by the authors were used. The evaluation was performed on the HDR-EYE dataset to create HDR images. Table 3 shows the PSNR and SSIM scores on the tone-mapped images. We used 46 images for evaluation. The table shows that our approach can provide comparable results to the previous methods. In the PSNR metric, our results were better than HDRCNN and DrTMO. The proposed technique also performed better than HDRCNN in SSIM. Figures 8-11 show the tone-mapped results of single-image HDR reconstruction obtained through the previous methods and the proposed approach. Some underexposed areas were successfully reconstructed in all images, but some minor differences existed in hue and lightness. HDRCNN tended to variegate the image, but can also over-enhanced dark areas, resulting in a loss of contrast. The output from DrTMO, on the other hand, had blurred and washed-out tones and failed to faithfully represent the true colors of the scene. ExpandNet and SingleHDR cannot recover locally underexposed areas well. As shown in the results, the images obtained using the proposed approach restored details in dark areas, preserved contrast, and provided vivid color information.