# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 1 Introduction  
As artificial intelligence advances in modern society, automation and unmanned technologies are being introduced in various fields. In particular, intelligent CCTV systems, autonomous driving, and remote monitoring are composed of various computer vision algorithms. These systems require excellent visibility in the acquired images for high accuracy. However, objects may not be clearly identified under insufficient contrast in images acquired at night or due to environmental factors such as ambient light sources. Various low-light image enhancement algorithms have been proposed to address these issues.  
A common approach for improving low-light images has been to enhance dark areas by expanding the dynamic range of the histogram in the image. However, in images with non-uniform lighting, this can lead to over-enhancement of bright areas, thereby corrupting data in bright areas [1; 2]. [PERSON] et al. proposed an adaptive gamma correction method using a weight distribution to compensate low-light images, but it resulted in the over-enhancement of bright areas in the image [3].  
Low-light image enhancement using the Retinex model divides an image into illumination and reflection components and uses these components to estimate or generate an illuminated image using various techniques [4; 5]. For low-light footage enhancement to produce the best quality results, the lighting and reflections must be in ideal conditions [6]. [PERSON] et al. applied a joint edge-preserving filter to the illumination to obtain improved images [7]. [PERSON] et al. used a structure-aware smoothing technique to predict the illumination component in an image based on the Retinex model [8].  
Traditional methods often fail to produce visually natural results. Some methods over-emphasize low-light areas, resulting in unnatural outcomes or even ruining the overalltonality of the input image. Moreover, incorrect estimates of illumination can lead to color mismatch issues in the resulting images, which can sometimes reduce visibility [9; 10].  
To address these issues, an algorithm has been proposed in this paper using illumination component estimation and a local steering kernel for low-light image enhancement. To maintain the naturalness of the image even when there are bright areas in the low-light image, the proposed algorithm estimates the illumination component by calculating the gamma value using the mean and distribution constant obtained from the histogram of the image. Then, the image with improved illumination is obtained using Retinex theory, and color correction is performed by comparing the pixel values of the RGB channels of the pixel to each other to emphasize the colors of the improved image. Finally, a local steering kernel is used for filtering to remove the co-amplified noise components during the low-light image enhancement process.  
The paper is structured as follows. Section 2 introduces related research on low-light image enhancement, and Section 3 explains the process by which our proposed algorithm improves low-light images. Section 4 discusses the performance of our proposed low-light image enhancement algorithm based on simulation results, and finally, Section 5 presents the conclusions.