A dim and small target detection method based on surfacelet transform is proposed to improve the performance of dim and small target detection under the complex clouds background. Firstly, the original infrared image is decomposed by the surfacelet transform to extract and analyze the multi-scale and multi-directional characteristics of the image. Then, the total variation and the local mean removal method are utilized to process the high-frequency and the low-frequency sub-bands respectively, which refines the coefficient value of the decomposed sub-bands. Finally, the refined sub-bands are recostructed to make the dim and small target separate from the background clutter signal, and then the background suppression is achieved and the real target is detected effectively. Theoretical analysis and experimental results show that, compared with the wavelet transform method and the total variation method, values of ISCR and BSF of the experimental result by the proposed method is higher, and the result by the proposed method has better effect both in subjective vision and the objective numerical evaluation.
A novel dim small target detection algorithm based on the nonsubsampled contourlet transform (NSCT) and the singular value decomposition (SVD) is proposed in this paper, which is to improve the performance of the dim small target detection under the complex sky cloud background. Firstly, the original infrared image is decomposed with the SVD, and several different numbers of the singular value for reconstruction is chosen to analyze the application of the SVD to the image. The complex sky cloud background in the infrared target image is predicted by choosing a certain number of the singular value to reconstruct the image, and it is subtracted from the original image to suppress the background and enhance the target signal. Secondly, to use the scale and the direction information of the image, the residual image is decomposed by the NSCT into several high-pass directional subbands and a low-pass subband. Thirdly, the SVD filtering is utilized again to those directional subbands to eliminate the noise and the residual background. And the low-pass subband is modified by the local mean removal method. Finally, the refined subbands are reconstructed by the inverse NSCT to fulfill the dim small target detection. The experimental results demonstrate that the proposed algorithm has better subjective vision and objective numerical indicators, and can acquire a better performance of the target detection.