# Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network  
[PERSON]  
1 College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; [EMAIL_ADDRESS] (O.G. ); [EMAIL_ADDRESS] (J.G.) [PERSON]  
1 College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; [EMAIL_ADDRESS] (O.G.) [PERSON]  
1 College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; [EMAIL_ADDRESS] (O.G.) [PERSON]  
1 College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; [EMAIL_ADDRESS] (O.G.) Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/10](https://www.mdpi.com/journals/pdf/10)]([https://www.mdpi.com/journals/pdf/10](https://www.mdpi.com/journals/pdf/10))  
range (generally k = 2) and low precision of refocusing the foreground and background. However, LF cameras [11] refocus using LF sub-aperture images (SAIs), which have a wide depth range (generally k = 20-40) and high precision of pixel-level refocusing. Nevertheless, an LF image acquired using an LF camera has low image quality in terms of luminance contrast. Consequently, LF digital image enhancement has become the main research topic for obtaining information from image structures and to better interpret images. In recent years, image contrast has been a popular research topic [12; 13]. Contrast enhancement [14; 15] is an important preprocessing procedure for a range of applications, such as remote sensing [16], medical imaging [17], and underwater imaging [18]. Among the numerous enhancement algorithms, histogram equalization (HE) is diffusely used [19; 20]. However, HE is usually used to enhance the global contrast without selecting the data to process [21], which may lead to adverse effects, such as color bleeding, unnaturalness, a halo effect, or over-enhancement [22]. In addition, the existing contrast enhancement schemes are all for 2D images, and there is no research on LF image processing. Therefore, a real-time and intelligent algorithm with which to heighten the contrast is required, which will improve the LF image quality and boost information representation. We present a content-adaptive LF contrast enhancement method to boost the information expression, and the adaptive parameters are predicted by a hierarchical network in this article. First, the database images are divided into four categories according to the image feature information. Then, we take the multilayer perceptron (MLP) based on a previous classification to obtain the adaptive parameters and enhance the image contrast intelligently. This method realizes batch dispose and automatically operates on multiple images, which greatly improves the image processing speed. As illustrated in Figure 1, focal stack comparison results show the effectiveness of our scheme. The global contrast of the enhanced FS is significantly improved. The enhanced focus stack obtained by our method has better image quality for dark areas. The texture of the greenery is depressed in (b) but clear in (c). The over-enhanced regions in (b) are recovered by our adjustments. Most notably, the application of our proposed contrast algorithm to LF image enhancement is an original and novel contribution, to the best of our knowledge. A preliminary version of this paper was published at an international conference [23]. This paper provides an in-depth discussion, a more complete description, and supplementary experimental results. The main contributions of the work are as follows:  
1. This paper proposes a novel, adaptive LF contrast enhancement method by using an MLP to train the dataset, which automatically adapts to mapping the curve parameters of the different views, adjusts the image ulteriorly, and enhances the contrast.