Compressed sensing is an arisen and significant theory, which has been widely used in infrared image reconstruction and many methods based on compressed sensing have been proposed. However, the existing methods can hardly accurately reconstruct infrared images. Considering that the sparsity of an infrared image plays a crucial role in compressed sensing to accurately reconstruct image, this paper presents a new sparse representation (MBFSF) that integrates the multiscale bilateral filter with shearing filter to overcome the above disadvantage. Firstly, one approximation subband image and a series of detail subband images at different scales and directions are obtained by the MBFSF. Then, in view of the feature that the most information is preserved in the approximation subband image, the proposed method only measures the detail subband images and preserves the approximation subband image. Subsequently, a very sparse random measurement matrix is used for the measurement at the detail subband images to reduce the computation cost and storage of large random measurement matrices in compressed sensing. Finally, an accelerated iterative hard thresholding algorithm is employed to reconstruct the infrared image. Experimental results show that the proposed method has superior performance in terms of reconstruction accuracy and compares favorably with existing compressed sensing methods, which is an effective method in high-resolution infrared imaging based on compressed sensing.
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.