A fast infrared image enhancement algorithm based on contrast optimization model

In infrared image contrast enhancement, plateau histogram equalization is a fast algorithm with good performance, and the key lies in how to choose the appropriate plateau value. In order to address the shortcomings of existing plateau histogram algorithms and meet the needs of both high-performance and real-time processing, an optimization model for the triple plateau histogram algorithms based on contrast as an evaluation parameter is proposed. Firstly, in order to prevent excessive enhancement, the adaptive preprocessing is performed on the first plateau based on the image characteristics. Secondly, in order to achieve a balance between image enhancement and detail protection, a constraint criterion between the first and second plateaus was proposed. Then, in order to control the dynamic range of the resulting image, a third plateau value is set for the grayscale level that is in the bright area and has a probability density of zero. Finally, the present optimization model is applied to globally constrain the three plateau values, and the optimal plateau values are obtained through traversal optimization. Qualitative and quantitative experiments are conducted on several public databases, and the results show that the algorithm proposed in this paper has relatively better subjective results and objective metrics comparing with six existing plateau histogram algorithms. For 8 bit images, the processing time of the present algorithm is about 0.02 seconds, which has high real-time performance.