A multiscale differential fractal feature of an image is proposed and a small target detection method from complex nature clutter is presented. Considering the speciality that the fractal features of man-made objects change much more violently than that of nature's when the scale is varied, fractal features at multiple scales used for distinguishing man-made target from nature clutter should have more advantages over standard fractal dimensions. Multiscale differential fractal dimensions are deduced from typical fractal model and standard covering-blanket method is improved and used to estimate multiscale fractal dimensions. A multiscale differential fractal feature is defined as the variation of fractal dimensions between two scales at a rational scale range. It can stand out the fractal feature of man-made object from natural clutters much better than the fractal dimension by standard covering-blanket method. Meanwhile, the calculation and the storage amount are reduced greatly, they are 4/M and 2/M that of the standard covering-blanket method respectively (M is scale). In the image of multiscale differential fractal feature, local gray histogram statistical method is used for target detection. Experiment results indicate that this method is suitable for both kinds background of land and sea. It also can be appropriate in both kinds of infrared and TV images, and can detect small targets from a single frame correctly. This method is with high speed and is easy to be implemented.
An automatic target detection algorithm is developed for infrared image small target in complicate background of sea and sky. Wavelet multiresolution edge detection algorithm is adopted to detect the sea-level line from coarse to fine. A strip can be decided accordingly as the potential area where the naval vessel targets in infrared images usually appear. We realized target detection by defining an energy function that integrated the results of horizontal wavelet transform. We complete the detection by marking the position where the energy is assumed the maximum and improving the target location precision by comparing the dissimilarity between a couple of windows. Experiment results indicate that the method can detect and locate small targets precisely in an infrared image with high detection probability.
Three kinds of image reconstruct algorithms for Electrical Resistance Tomography (ERT) has been researched, and a new ERT reconstruct algorithm-Regularized general inverse(RGI) ERT reconstruct algorithm is proposed, which is based on linearity ERT forward problem, and makes use of general inverse to confirm the minimum norm error solution of ERT inverse problem. Meanwhile, adopting regularized method to stabilized the numerical value. The observation operator is set up by multiple linear regression method. Three restriction conditions is brought to bear the optimum stabilization solution. The simulation result shows that reconstructed image can reflect the truth medium distribution in the field truly including different complex distributions. After filtering the images by unite bound for the same medium distribution, the average of CSIE image reconstructed by linear back project algorithm, sensitivity coefficient algorithm and regularized general inverse algorithm is 12%, 9% and 6% respectively. The result shows that the image quality reconstructed by regularized general inverse algorithm is improved in evidence than that of the other two algorithms. The calculate amount of regularized general inverse algorithm is same as one step sensitivity coefficient algorithm, the speed of reconstruction is fast.
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