Image contrast enhancement and brightness preservation based on an adaptive histogram correction framework
Weitao Deng, Guofu Xie · Applied Optics · 2025
Image enhancement is the basis for advanced vision tasks in computer vision. In this paper, we propose a new, to our knowledge, histogram modification-based method for image contrast enhancement and global luminance preservation, in which we introduce an adaptive histogram modification framework and combine it with adaptive gamma correction. Both histogram modification and adaptive gamma correction are effective contrast enhancement methods. However, traditional methods based on histogram modification and adaptive gamma correction usually require the setting of parameters to be determined, which results in enhancement effects that are dependent on the parameters set. In our method, the adaptive histogram modification framework is proposed for processing the histogram and fully preserving the features of the original histogram, after which we use the modified histogram, which preserves the information of the original histogram, for adaptive gamma correction, and finally obtain the pixel intensity mapping relationship by adaptive gamma correction. The enhancement results using this method maintain the overall brightness and suppress artifacts. Experiments comparing the proposed method with currently popular methods in some popular datasets have been carried out, and the results show that the proposed method achieves better or similar results to other methods.