Infrared image enhancement through saliency feature analysis based on multi-scale decomposition

To improve contrast between dim target region and background in infrared (IR) long-range surveillance, this paper proposes a fast image enhancement approach using saliency feature extraction based on multi-scale decomposition. Firstly, a smooth based multi-scale decomposition is designed and applied to original infrared image, generating sub-images with various frequency components at different decomposition levels. The dim target regions of sub-images are extracted by a local frequency-tuned based saliency feature detection method, secondly. With saliency maps created by saliency extraction using multi-scale local windows with different sizes, the sub-images are enhanced at different decomposition scales. Finally, the enhanced result is reconstructed by synthesizing the all sub-images with adjustable synthetic weights. Since salient areas are analyzed based on fast multi-scale image decomposition, IR image can be s enhanced with good contrast successfully and rapidly. Compared with other algorithms, the experimental results prove that the proposed method is robust and efficient for IR image enhancement.