Image enhancement under low luminance with strong light weakening
Beibei Feng, Yibin Tang, Lin Zhou, Ying Chen, Jinxiu Zhu · 2016
In low luminance images (night scene), it suffers from various problems, e.g., low overall brightness, poor contrast and serious lack of information. However, in some scenario, strong light also appears in these images. In this case, the regions of high and low luminance both exist, which introduces a more complicated situation for image enhancement. In this paper, we present an enhancement method via dark channel prior to adaptively improve the contrast of given images, especially those containing strong light. To fully use of image dehazing, haze images are firstly obtained from original low luminance images, where the transmittance template is sequentially estimated by the dark channel prior. Later, we design a modified mapping transmittance function to optimize such template, where the factor of strong light is well taken into account. Moreover, to compensate the detail loss in strong light areas, an optimal model is further built to improve the aforementioned template. With a set of dehazing manipulations, enhanced images are finally achieved. Experimental results show that the proposed algorithm not only improves the brightness and contrast of traditional low luminance images, but also can deal with images with strong light, where the regions of strong light are efficiently suppressed.