Adaptive enhancement of platforms above sea-level in infrared images based on clustering of wavelet coefficients

This study proposes an adaptive infrared image enhancement technique for platforms above sea-level based on clustering of wavelet coefficients. Feature vectors constructed from subband images are computed using discrete wavelet transform and similar feature vectors are grouped using clustering operation. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices used to multiply wavelet coefficients for the enhancement of the original image. In the paper, enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load.