Many low-light image enhancement methods have been proposed so far. LIME by Guo et al. (IEEE Trans. Image Process. 26(2):982–993 (2017)) is one of the sophisticated low-light image enhancement methods based on retinex theory. In retinex theory, an image is decomposed into the illumination and the reflectance. In LIME, the illumination is estimated and an output image is obtained by dividing the input image by the estimated illumination. LIME significantly enhances the contrast in dark regions. However, it tends to cause losing fine details in bright regions and noticeable noise in dark regions. In this paper, we propose a new low-light image enhancement method to cope with these problems. In the proposed method, the illumination is estimated by soft-closing using smoothed local histogram. The output image is obtained by applying the original noise mitigation scheme to the reflectance map. The proposed method can improve the contrast of the image overall while suppressing fine details lost in bright regions and noise amplification. In the experiments, the effectiveness of the proposed method is verified by comparing with various state-of-the-art low-light image enhancement methods.

迄今为止，已经提出了许多低光图像增强方法。郭等人的石灰。（IEEE Trans. Image Process. 26(2):982–993 (2017)）是一种基于 Retinex 理论的复杂低光图像增强方法。在视网膜理论中，图像被分解为光照和反射。在 LIME 中，估计光照并通过将输入图像除以估计光照得到输出图像。LIME 显着增强了黑暗区域的对比度。但是，它往往会导致明亮区域的精细细节丢失和黑暗区域的明显噪点。在本文中，我们提出了一种新的低光图像增强方法来解决这些问题。在所提出的方法中，使用平滑的局部直方图通过软关闭来估计光照。输出图像是通过将原始噪声缓解方案应用于反射图获得的。所提出的方法可以提高图像的整体对比度，同时抑制明亮区域的细节丢失和噪声放大。在实验中，通过与各种最先进的低光图像增强方法进行比较，验证了所提方法的有效性。