Because of the platform motion and system internal asymmetric structure, Satellite-borne infrared imaging system will generate image geometric distortions such as translation, rotation, distortion and scaling, which make the subsequent target detection result not accurate. Therefore, we propose an image distortion method and deeply analyze the influence of infrared image distortion on the SNR of infrared weak small targets, detection probability and false alarm probability. The simulation results show that the image distortion directly affects the subsequent performance of the infrared target detection and tracking algorithm by changing target geometric imaging and signal to noise ratio. The research result in this paper would have great application value in the satellite-borne infrared alarm/warning system.
The target is moving and changing in infrared image sequences captured from the airborne platform infrared imaging system. To adaptively track the infrared target which changes from small target to surface target, an algorithm based on Second-Order Differential (SOD) and improved Template Matching (TM) tracking algorithm was proposed. The SOD filter makes full use of the brightness of the infrared dim and small target, the gradient and distance information of neighborhood pixels used for spatial domain filter. The TM makes full use of infrared brightness, ambient background and dimension information to complete the tracking. The experimental results show that the proposed algorithm can convert adaptively with infrared target’s size changing information, so tracking stability of infrared target under the ground clutter background is achieved. The tracking accuracy and tracking speed are also better than traditional algorithms. The proposed algorithm can be well applied to airborne platform warning on the ground.
In this paper, a curvature filter and PDE based non-uniformity correction algorithm is proposed, the key point of this algorithm is the way to estimate FPN. We use anisotropic diffusion to smooth noise and Gaussian curvature filter to extract the details of original image. Then combine these two parts together by guided image filter and subtract the result from original image to get the crude approximation of FPN. After that, a Temporal Low Pass Filter (TLPF) is utilized to filter out random noise and get the accurate FPN. Finally, subtract the FPN from original image to achieve non-uniformity correction. The performance of this algorithm is tested with two infrared image sequences, and the experimental results show that the proposed method achieves a better non-uniformity correction performance.
The drawback of temporal high-pass non-uniformity correction algorithm, ghosting and the image blurring, severely degrades the correction quality. In this paper, an improved non-uniformity correction algorithm based on shearlet transform is proposed. First, the proposed algorithm decomposes the original infrared image into one low frequency sub-band and a group of high frequency sub-bands by the shearlet transform. As a powerful mathematical tool, the decomposition of image by shearlet can reveal the detail of the image accurately. As the high frequency sub-bands contain the most of FPN, the FPN is estimated from the high frequency sub-bands by temporal high-pass. Then, the goal of non-uniformity correction can be achieved by subtracting the estimated FPN from the original high frequency sub-bands. At last, the corrected infrared image can be obtained by the inverse shearlet transform. The performance of the proposed algorithm is thoroughly studied with real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
The existence of non-uniformity is almost universal in the imaging process of the infrared system. By analyzing the mechanism of the non-uniformity, a temporal non-uniformity correction algorithm is proposed in this paper. First, the non-uniform image is filtered by the bilateral filter. Second, the filtered image is corrected using the moment match method. Finally, the corrected infrared images are acquired by iterating the moment matching image sequence in the time domain. Experiment shows that the proposed algorithm is superior to some existing methods both in experimental data and vision quality.
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.
The target tracking by the spatio-temporal learning is a kind of online tracking algorithm based on Bayesian framework. But it has the excursion problem when applied in the infrared dim target. Based on the principle of the spatio-temporal learning algorithm, the excursion problem was analyzed and a new robust algorithm for infrared dim target tracking is proposed in this paper. Firstly, the Guide Image Filter was adopted to process the input image to preserve edges and eliminate the noise of the image. Secondly, the ideal spatial context model was calculated with the input image that contains little noise, which can be got by subtracting the filtering result from the original image. Simultaneously, a new weight in the context prior model was proposed to indicate that the prior is also related to the local gray level difference. The performance of the presented algorithm was tested with two infrared air image sequences, and the experimental results show that the proposed algorithm performs well in terms of efficiency, accuracy and robustness.
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 core technology in the infrared warning system is the detection tracking of dim and small targets with
complicated background. Consequently, running the detection algorithm on the hardware platform has highly practical
value in the military field. In this paper, a real-time detection tracking system of infrared dim and small target which is
used FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) as the core was designed and the
corresponding detection tracking algorithm and the signal flow is elaborated. At the first stage, the FPGA obtain the
infrared image sequence from the sensor, then it suppresses background clutter by mathematical morphology method and
enhances the target intensity by Laplacian of Gaussian operator. At the second stage, the DSP obtain both the original
image and the filtered image form the FPGA via the video port. Then it segments the target from the filtered image by an
adaptive threshold segmentation method and gets rid of false target by pipeline filter. Experimental results show that our
system can achieve higher detection rate and lower false alarm rate.