# Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
## 2 Related Work  
### LF Imaging  
As illustrated in Figure 2a, LF imaging records light [24] by adding a microlens array and subsequently performs digital zooming using a post-processing algorithm (Fourier slice theorem, LF imaging algorithm). According to the principle of LF imaging, refocused or sub-aperture images are realized based on the recorded light to obtain LF images for research. [PERSON] et al. [25] proposed an efficient auto-refocusing (ARF) solution for LF cameras, where only three refocus renders are required to find the best refocus frame. [PERSON] et al. [2] presented an LF refocusing solution to improve image quality. This method can selectively refocus the image, super-resolve the focused region, and aesthetically render the bokeh. [PERSON] et al. [26] put forward a neural network digital refocusing algorithm based on sparse angular information; its structure is relatively small and can be quickly reconstructed with low memory consumption.  
Figure 1: Comparison of the effect of the contrast enhancement method. (**a**) Original LF focal stack; (**b**) the focal stack obtained after HE; (**c**) our resulting focal stack.  
In our experimental study, several classes of scenes were taken using a Lytro-Illum camera (using a tripod as an auxiliary tool) in order to establish a new dataset. The camera parameter set, scenes, and features of the LF image (sunshine, cloudy, and indoor) are illustrated in Table 1 below. The camera saved the gained data in files with the suffix LFP (about 55 MB each), which contained several types of data (such as RAW images, thumbnails in BMP format, system settings, etc.). The RAW image is 10-bit packages, with a total resolution of 7728 \(\times\) 5368 in GRBG format. The Light Field Toolbox for Matlab v0.5 [27] was utilized to process the RAW files. The package generated our required 5D files, as well as other additional files (such as color-corrected images, etc.), by decoding and correcting the LF data, using the camera's calibration information (a series of white image files taken from the Lytro-Illum's external memory card).  
The output of the decoding was the reconstructed LF corresponding to the square grid of the microlens (5D file above). The square grid consisted of 625 \(\times\) 434 microlenses, and each microlens captured light information from 15 \(\times\) 15 different directions. The data were saved in the matrix LF (i, j, k, l, c) of size (15, 15, 434, 625, 4) [28]. Figure 2b shows the reconstructed light field; the original data decoded by the LF camera comprises the individual circular areas. The reason for this is that each microlens images the circular main lens, and the final image produced by the LF camera is actually the sum of the pixels that correspond to each microlens. In other words, one microlens is one pixel of the final LF image. However, the size limitation of the microlens results in the low resolution and low quality of the final imaging.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Shutter** & **ISO** & **F-Stop** \\ \hline Sunshine & 1/300–1/400 & 80–150 & f/2 \\ Cloudy & 1/100–1/250 & 125–240 & f/2 \\ Indoor & 1/30–1/80 & 200–400 & f/2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lytro-Illum camera parameter set, scenes, and features of LF images.  
Figure 2: (a) Light field imaging principle; (b)original image decoded by LF camera.