Small target is also weak target, which is likely to be a threat to the observation platform. So small target detection is an important task for many automatic object detection system. Otherwise, small target detection is a challenge for many complex scenes because of the low SNR and sophisticated background. This paper introduced a fast and effective method for small target detection in infrared scene with complex background, which is suitable for missile guidance and menace warning. Firstly, a template is created to detect the local maxima in the image. Secondly, a constrained double criteria region growth algorithm is performed to form separate regions. Finally, extracted regions are selected by a small round target filter, after which, the remaining connected regions are considered to be detected small targets. The proposed algorithm was applied on videos captured by cooled infrared imagers. Experimental results show the method introduced in this paper is efficient and effective, which is suitable for time sensitive automatic target detection.
The spectral characteristics of infrared radiation from target provide significant characteristics information for target's detection and track including radiance brightness, radiance intensity and spectrum characteristics of target. And the same time, the spectral characteristics provide the basis of target detection and recognize equipment's waveband optimization design and detection capability analysis. This paper using the passive imaging Fourier transformation infrared spectrometer measure the infrared spectral characteristic of target. The spectral range cover the medium wave and long wave infrared. And the instrument can interference imaging in 320×256 spatial resolution or other window size. This paper designs a set of calibration and test processes to realize the infrared spectral radiance measurement of target. Using this method, this paper test some typical infrared target. After the radiance calibration, the calibrated result is verified by standard radiance source. Thereby, the remote measurement of infrared background is taken as the comparison test. Finally, the typical infrared target spectral features are extracted and measured. The test results show that the method mentioned in this paper is practical.
In order to improve the characteristic information of the fused images, we propose a novel infrared and visible image fusion algorithm based on image detail enhancement in this paper, the bilateral filter and dynamic range partitioning (BF & DRP) are used to improve the original infrared image, and the multi-scale retinex transform (MRT) also is used to deal with image fusion. <p> </p>Firstly a method of bilateral filter and dynamic range partitioning (BF & DRP) was used to improve the details of the low SNR and low contrast original infrared image, by which the edges of targets were strengthened, the noises were suppressed, and the constrast of infrared image was enhanced. Secondly, and finally, the multi-scale retinex transform was used to improve the fusion of visible and infrared image, by combining the multi-scale transform and regional fusion where the adaptive low frequency and high frequency coefficient were considered, which effectively suppressed the noises and enhanced the details.. <p> </p>Experimental results proved the effectiveness of the proposed image fusion method. The salient color and texture feature of visible image was well preserved, the important details of infrared and visible image were highlighted. The results show that this algorithm is better than traditional image fusion method, such as wavelet transform, non-sampled contourlet transform, in in standard deviation, information entropy and Average gradient etc.. the algorithm of this paper is able to preserve the details of image, increase the amount of importance characteristic information, is advantageous to the visual performance and distinguishability of fused image for human observation.
In this paper, an infrared and color image fusion algorithm based on luminance–contrast transfer technique is presented. This algorithm shall operate YCbCr transform on color visible image, and obtain the luminance component. Then, the grey-scale image fusion methods are utilized to fuse the luminance component of visible and infrared images to acquire grey-scale fusion image. After that, the grey-scale fusion image and visible image are fused to form color fusion image based on inversed YCbCr transform. To acquire better details appearance, a natural-sense color transfer fusion algorithm based on reference image is proposed. Furthermore, a real-time infrared/visible image fusion system based on FPGA is realized. Finally, this design and achievement is verified experimentally, and the experimental results show that the system can produce a color fusion image with good image quality and real-time performance.
By analyzing the characteristics of infrared focal plane array image, an improved implementation of infrared focal
plane image enhancement algorithm based on FPGA is proposed, with limited FPGA memory resources for gray-scale
stretching. Experiment results show that the implementation is easy on FPGA with low FPGA memory without extra
memory devices. Moreover, it is flexible and effective for improving gray contrast of the interested region of the
image, and proved to meet the requirements of infrared focal plane detector for image enhancement showing great
According to the configuration and technical specification of the detector, which has multiple channels, channels mixing,
high speed outputs and separate columns between odd and even, a real time digital processing unit based on the CPLD,
FPGA and DSP has been developed to achieve the data synthesis and arrangement function and the parity correction
algorithm. A special interface circuit with 4 CPLDs is designed to complete the first synthesis step where the 16 channels
of data are combined into 4 channels. The second step is finished in FPGA and ROM address encoder where the 4
channels of data are combined into 1 channel. For output data synchronization, FIFO is adopted to achieve the delay of
even channels in the parity correction. Data of odd channels enters the columns synthesis unit without any processing
and even channels shall be processed in the columns synthesis unit after entering the FIFO unit first and experiencing the
delay process. Thereby the pre-processing before image processing of the linear array thermal imager is accomplished.