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Based on human visual attention mechanisms, an automatic detection algorithm for infrared dim target is presented.
The dim target regions of sub-images are extracted by a local frequency-tuned based saliency feature detection method, secondly.
Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc.
After analyzing the characteristics of infrared dim target images, the method firstly designs Difference of Gaussians (DoG) filters to compute the saliency map.
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.
We believe our results confirm the suitability of utilising additional small, dim target for discrimination of more subtle deficits.
Similar(53)
Long-range infrared images are always suffering from dim targets and background clutters.
The detection and discrimination of infrared small dim targets is a challenge in automatic target recognition (ATR), because there is no salient information of size, shape and texture.
Compared with several state-of-arts methods, our algorithm is more effective for the dim targets with lower signal-to clutter ratio (SCR).
But if Kepler was a telephoto aimed at dim targets far in the distance, TESS is an ultra-wide-angle lens that will watch nearly the entire visible sky.
In order to suppress the strong edges while preserving the dim targets, a weighted infrared patch-image (WIPI) model is proposed, incorporating structural prior information into the process of infrared small target and background separation.
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