Robust non-uniform illumination correction for instrument images based on improved K-means clustering and an adaptive...

In petrochemical environments, dynamic illumination fluctuations severely degrade inspection image quality, leading to inaccurate instrument readings. To address this issue, a non-uniform illumination correction method is proposed that integrates improved K-means clustering with an adaptive 2D Gamma function. The clustering step adaptively segments illumination levels via histogram peak-valley extraction. The results then guide Gaussian scale selection and adaptive weighting for illumination estimation. Finally, the adaptive 2D Gamma function is dynamically optimized via parameter estimation and monotonicity constraints. Extensive experiments under varying illumination conditions demonstrate the robustness of the proposed method, maintaining entropy values above 7.05, average gradients ranging from 4.05 to 8.79, and NIQE scores as low as 3.21. The proposed method effectively corrects non-uniform illumination to facilitate subsequent instrument reading tasks.