# High-Brightness Image Enhancement Algorithm
## 1 Introduction  
Videos are frequently used to judge and identify information. They have been widely used in public security monitoring. However, according to weather conditions, luminance conditions, capture equipment and other causes, high-brightness areas may exist in a given video, which results in the information of the target not being obtained. The existence of the bright regions cannot meet the needs of applications. In particular, strong light sources (such as car lights, streetlights and flashlights) make it challenging to enhance night-time images. However, although the existing image enhancement algorithms have apparent effects in improving the details of low-light images, they do not suppress the high-brightness areas in the picture. The existence of bright regions seriously affects the display of the highlighted area's details and the observer's direct visual experience. To solve the abovementioned problems, we propose a method for video image processing based on tone mapping. The method is used to remove the bright part of the image, and it can restore the original details of the illuminated area while other areas with brightness remain. The examined video was composed of one image frame, so we focused on the enhancement of a single image. The information of the area with uneven illumination was recovered by the image enhancement algorithm. Although the dynamic range can be compressed by global linear scaling, the result is unsatisfactory because the image structure is flattened linearly, losing most of the visual information in light areas and shadows. Tone mapping is a technique for mapping high-dynamic range (HDR) images to display devices with low dynamic ranges. [PERSON] et al. [1] introduced the concept of tone mapping to computer graphics in 1993. [PERSON] et al. [2] modified and adjusted traditional histogram equalization technology and added some theories regarding the human visual perception system, such as contrast, sharpness and color sensitivity, which were inspired by the previous work of [PERSON] [3]. The method proposed by [PERSON] reduces the dynamic range of an image by simple linear scaling, so it cannot operate with strong dynamic range compression. [PERSON] et al. [4] proposed separating an image into high-frequency sub-images and low-frequency sub-images by using an approximate bilateral filter and a downsampling acceleration filter. The high-frequency part is called the detail layer, and the low-frequency part is called the basic layer, which can better preserve edges. This method can be applied to any global tone mapping algorithm, but after using this method, halation is not entirely eliminated. For the improvement of this method, trilateral filters and other methods have appeared. In 2002, [PERSON] et al. [5] proposed a tone mapping method based on photography. This algorithm (with a global component) mainly compresses the high-brightness part of an image. Photographic hue reproduction is a local tone mapping algorithm that retains the boundary effect and avoids halation. In addition, it has the advantage that it does not need to input corrected images. [PERSON] et al. [6] proposed a global mapping method in line with the human visual model, and this algorithm enhances the local contrast of images. In 2003, [PERSON] et al. [7] proposed a classical global tone mapping algorithm--the adaptive logarithmic mapping algorithm--which can adaptively adjust the cardinality of logarithmic equations according to the different brightness values of pixels. This method can reduce the dynamic range of the examined image, but it easily loses local details, and its effect on improving the overall contrast of the image is not apparent. [PERSON] et al. [8] proposed a novel hue mapping algorithm based on the fast adjustment of global histograms, which can effectively use the whole dynamic range of the display to reproduce the global contrast of high dynamic range images.