The original image bit width of a cooled thermal imaging camera is 14Bit. 14Bit has a wider grayscale range, higher accuracy, and more image details compared to 8Bit, but the maximum grayscale range that can be displayed by a general display is 8Bit, so the original image data of 14Bit needs to be compressed effectively. In this paper, we propose an image compression and display algorithm based on guided image filtering (GIF). First, the image is layered using guided image filtering, i.e., the base layer and the detail layer. Then the adaptive platform histogram equalization (APHE) process is applied to the base layer to improve the contrast of the image; the adaptive detail enhancement is applied to the detail layer to enhance the details while reducing the noise. Finally, the images processed separately after layering are linearly fused to achieve dynamic compression and detail enhancement of high Bit images. Through simulation and comparison experiments with commonly used algorithms, as well as comprehensive comparison of visual effects and quantitative evaluation parameters, the proposed algorithm in this paper achieves significant improvements in both performance and effectiveness.
The two-step method using Hypothesis-Generation two steps to early detect the low-small-slow target, is based on the theory of multi-source detection fusion. The Hypothesis-Generation of the target information is built by the data set of multi-sensor. Hypothesis-Union distinguishing of the target information is based on the method of multi-source decision fusion, is realized by distributed decision feature fusion based on Bias minimum risk rule. The experimental results verify the feasibility and effectiveness of the method.
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