# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 2 Related Work  
### Datasets  
General-purpose camera devices on the market cannot preserve the full dynamic range of a real scene within a single frame [1]. The dynamic range of the image outputs by the camera devices is limited because the imaging process undergoes many transformations [1]. The imaging process requires sensor and exposure adjustment, color space conversion, tone mapping, nonlinear conversion, digital quantization, and other conversions [1, 35, 36, 37]. Figure 1 outlines the conversion process that imaging must go through [1, 35, 36, 37]. After the block named Sensor & Exposure, high dynamic range (HDR) images will suffer from sensor noise pollution and exposure to reduce dynamic range. After the block named Color Gamut Mapping, the HDR image pixels will be truncated due to the color gamut mapping. After the block named Quantization, HDR images are further degraded due to conversion to 8-bit digital image data and compression. The final standard dynamic range (SDR) image is the original HDR scene after multiple degradations. Since general-purpose camera devices cannot capture the full dynamic range of a scene, the limited dynamic range of the image affects the image quality. For example, the e shows a full wide dynamic range in Figure 2. Obviously, it displays various brightness levels. The a-d in Figure 2 represent the dynamic range of a part of the scene captured by the camera, so the brightness level that can be displayed is relatively truncated. By observing Figure 2, we can more clearly understand the difference between the image with limited dynamic range and the original scene at the same exposure time. Obviously, the higher dynamic range image can fully preserve the details in both high-brightness and low-brightness areas. Oppositely, a limited dynamic range image cannot preserve image details in both the high-brightness area and low-brightness area at the same time. In order to record more image details, people usually set the camera to retain the middle dynamic range, such as b or c in Figure 2. Another factor that can affect the dynamic  
Figure 1: The conversion from an HDR scene to an SDR image. range of an image is exposure time. We take the 145 th scene in the Tursen dataset [38] as an example and illustrated in Figure 3.  
Figure 3: The illustration of a high dynamic range image. Figure 2: The illustration of dynamic range. Figure 3a is the HDR image generated by using Photomatix free version [39], fusing LDR images with shutter time 1/512, 1/128, 1/64, 1/32, and 1/16. Figures 3b-f and 3h-l are taken from the shutter time 1/512, 1/128, 1/64, 1/32, and 1/16 LDR images, respectively. Figures 3g and 3m are, respectively, taken from the fused HDR image. By observing Figure 3b-m, we can easily understand that different exposure times produce different dynamic ranges and affect the preservation of image details [40; 41; 42]. Generally speaking, a longer exposure time will result in a brighter overall image, and more image details in the dark areas will be retained. On the contrary, when the exposure time is shorter, the overall image brightness is darker, and more image details in the bright area can be retained. We draw the histograms of Figure 3b-g, respectively, as shown in Figure 4a-f. Furthermore, we draw the histograms of Figure 3h-m, respectively, as shown in Figure 5a-f. Figure 4a-e illustrates areas with relatively low brightness of the HDR image in Figure 3. Since the shutter time of Figure 4a is shorter than that of Figure 4e, Figure 4a can hardly retain the image details in the image. We call this phenomenon as a low-light saturation. The histogram of Figure 4a shows that all pixels are close to the minimum brightness. Low-brightness LDR images cannot retain low-brightness image details. In other words, the low-brightness areas in LDR images are prone to detail distortion. Figure 5a-e illustrates the areas with relatively high brightness in Figure 3a.