A spatially adaptive denoising with activity level estimation based method for infrared small target detection

In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning dim small target detection from infrared image, a spatially adaptive denoising with activity level estimation based method is adopted here for infrared small target detection. First, the original image is decomposed into different levels of coefficients using DWT method and these coefficients are reconstructed to wavelet subband images. Second, based on the least squares estimate criterion, the weighted average of neighborhood point is used to get the activity level of current point. Third, the points with similar activity level are estimated in energy to get adaptive thresholding, thus to achieve the soft-threshold denoising in wavelet domain. Finally, a simple threshold segmentation method is used to get the dim small target. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.