In this paper, a new temporal high-pass filter nonuniformity correction algorithm based on guided filter is proposed, which address the ghosting artifacts and preserve image details of original image. In this algorithm, the original input image is separated into two parts, which are the high spatial-frequency part that contains most of the nonuniformity and the low spatial-frequency part with well preserved details. Then the fixed pattern noise is estimated from the high spatial-frequency part and subtracted from the original image, which achieves the nonuniformity correction. The performance of this presented algorithm is tested with two infrared image sequences, and the experimental results show that the proposed algorithm can significantly reduce the ghosting artifacts and achieve a better nonuniformity correction performance.
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.
Influenced by detectors’ material, related manufacturing technology etc, every detection element’s responsivity in infrared focal plane arrays(IRFPA) is different, which results in non-uniformity of IRFPA. So non-uniformity correction(NUC) is an important technique for IRFPA. The classical two-point NUC algorithm based on reference sources is analyzed in this paper. And a new NUC algorithm based on statistical characteristics of image serial is presented. In this algorithm, the reference images are constructed from image serial, and correction parameters are computed by using the constructed reference images. Then two-point NUC is applied to output images of IRFPA. Experimental results show that the algorithm proposed in this paper is effective and implemented easily.
Hyperspectral image (HI) contains data in hundreds of narrow contiguous spectral bands, thus it provides a powerful means to distinguish different materials on the basis of their unique spectral signatures. Anomaly detection (AD) is one key part of its application. The shearlet transform (ST) is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks, which can effectively captures smooth contours that are the dominant feature in natural image. In this paper, ST is used in AD for the HI. Firstly, the raw HI data is decomposed into several directional subband at multiple-scale via ST. Thus, the background signal would be reduced in each subband. Secondly, the fourth partial differential equation method is adopted to modify the coefficient of each sub-band, which is for background suppression and anomaly signal enhancement. Thirdly, the kernel-based RX algorithm is adopted to detect the anomaly in each sub-band. Finally, the anomaly signal image is achieved by reconstructing the image with all modified sub-band. Several experiments with a HYDICE data are fulfilled to validate the performance of the proposed method. Compared with the original RX algorithm, experimental results show that the proposed algorithm has better detection performance and lower false alarm probability.
Complex background suppression is a key problem in the detection of the infrared dim small target at far distance. In this paper, a background suppression method for the dim small target detection based on the combination of the high-order diffusion equation and the RX operator is proposed. Firstly, the high-order diffusion equation is applied to decompose the original infrared image, and the multiscale features of the image are extracted. Then, by the fact that the signal coefficients of target are different with that of background clutter in the decomposed sub-image, the RX operator is utilized to separate the dim small target and the background clutter. Two groups of experimental results demonstrate that the complex background can be suppressed by the presented method effectively, whose performance is better than that of the max median (MMed) method. The proposed method can preserve and enhance the infrared target signal effectively whose SCR is greater than 1.7.