A new grey mapping function and its adaptive algorithm for low-light image enhancement

When taking images in low light conditions, images often suffer from low visibility. In addition to affecting the sensory quality of images, this poor quality may also significantly limit the performance of various computer vision systems. Many grey-level mapping enhancement algorithms based on classic mapping functions, such as the gamma mapping function, have been proposed in recent years to improve the visual quality of low-light images. However, the classic mapping function cannot coordinate the greyscale distribution of the bright and dark areas of the image well and may easily lead to excessive enhancement. This makes it difficult for the performance of these improved algorithms to be fully utilized. Therefore, this paper proposes a new multiparameter grey mapping method. Unlike the classic mapping function, the new mapping method is based on the enhancement strategy of compressing the bright area and then adjusting the dark area. Thus, the inherent shortcomings of the classic mapping function are fundamentally overcome. The new mapping method can not only directly control the compression of the grey space in the bright area of the image through parameters, but it can also adjust the greyscale distribution of dark areas without changing the greyscale value of the pixels in the bright area. Finally, this paper also designs an adaptive enhancement algorithm with the new mapping method as the core to verify its effectiveness and flexibility. Experimental results showed that the adaptive algorithm had excellent performance in colour rendering, brightness enhancement and noise suppression. It was also obviously better than the current similar algorithms in visual quality and quantitative tests.