Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues.

Image enhancement is an important preprocessing step of infrared (IR) based target recognition and surveillance systems. For a better visualization of targets, it is vital to develop image enhancement techniques that increase the contrast between the target and background and emphasize the regions in the target while suppressing noises and background clutter. This study proposes what we believe to be a novel IR image enhancement method for sea-surface targets based on local frequency cues. The image is transformed blockwise into the Fourier domain, and clustering is done according to the number of expected regions to be enhanced in the scene. Based on the variations in the elements in any cluster and the differences between the cluster centers in the frequency domain, two gain matrices are computed for midfrequency and high frequency images by which the image is enhanced accordingly. We provide results for real data and compare the performance of the proposed algorithm through subjective and quantitative tests with four different enhancement methods. The algorithm shows a better performance in the detail visibility of the target.