A low-illumination image refers to a partially or globally dark image, e.g., an image taken under relatively low illumination. The low-illumination image has poor visibility, which seriously affects observation of people and performances of some computer vision algorithms. The computer vision algorithms usually require that inputted images have relatively high visibility. Most algorithms cannot directly process the low-illumination images. Therefore, some low-illumination images often need to be enhanced before performing corresponding operations. In order to solve this problem, many algorithms for enhancing the low-illumination images have been provided. Low-illumination enhancement algorithms enable the enhanced images to have higher visibility by changing pixel brightness of the inputted images. Existing low-illumination enhancement methods are mainly classified into four types as follows.
I. A method for low-illumination enhancement by mapping with nonlinear equations: some nonlinear monotone equations, such as power functions, logarithmic functions, exponential functions, etc., are used for gray-level mapping.
II. A method for low-illumination enhancement by equalization of histograms: on account of uneven distribution of the histograms of the low-illumination images, this method enhances the low-illumination images by the equalization of the histograms and makes the images have relatively good visibility by changing contrast of the images; however, the method may cause distortion of the enhancement result due to over-enhancement of the contrast of the images.III. A method for low-illumination enhancement by utilizing a retinal theory: the retinal theory enhances the low-illumination images by dividing the images into two components including irradiance and reflection; and this method can apparently enhance image details, but a halo phenomenon often appears in the enhanced images.IV. A method for low-illumination enhancement based on a defogging theory: such methods can achieve good subjective results, but can also cause some color distortion due to over-enhancement of the contrast.
In general, the existing methods for enhancing low-illumination images may introduce some artificial traces, such as color distortion, contrast distortion, etc., while enhancing the images, and are hard to obtain the enhanced images preserving naturalness, thereby not only affecting subjective visual perception of people, but also affecting the performance of the computer vision algorithms.