48 results about "Exponential transformation" patented technology

The invention discloses an SAR (Synthetic Aperture Radar) image speckle reduction method based on sparse representation, which mainly solves the problems of incomplete detail information and unsmooth homogeneous regions for SAR image speckle reduction in the prior art. The method comprises the following steps of: carrying out logarithmic transformation on an original SAR image Y; carrying out overlapping blocking on an image Y' subjected to logarithmic transformation; taking the own information of the image blocks as control factors and carrying out sparse representation on the control factors; carrying out dictionary learning by adopting an approximate KSVD (K Singular Value Decomposition) algorithm to obtain a self-adaptive dictionary and updated sparse representation coefficients; obtaining an image W by adopting the self-adaptive dictionary and the updated sparse representation coefficients, and carrying out exponential transformation on the image W to obtain an image R; and carrying out nonlinear anisotropic diffusion on a difference image V obtained from the original SAR image Y and the image R subjected to the exponential transformation, thereby obtaining a final speckle-reduced image. The method disclosed by the invention has the advantages that the homogeneous regions of speckle reduction results are smooth, point targets are clear and visible, and edge information iskept complete and the like, besides, the method is suitable for a preprocessing process of SAR image understanding.

The invention discloses an illumination normalization method for processing human face images, which is specifically implemented according to the following steps: read in the human face image to be processed and take logarithm of it; calculate the shadow layer image; calculate the reflective layer image and perform exponential transformation ; select the sample image and calculate its histogram; normalize the reflective layer image by using the histogram matching method to obtain the corrected image of the face image to be processed by the illumination normalization method. The feature information on the large-scale layer can be preserved by using the edge-preserving filter; the sample image in the visually optimal area is used, so that the mean and variance of the corrected reflection layer image can be in the visually optimal area, which is beneficial to the subsequent face The recognition system can improve the correct rate of face recognition, and solve the problem that the existing feature extraction method of invariant illumination causes a large loss of feature information during face recognition, which is not conducive to the subsequent face recognition.

The invention discloses a noise intensity self-adaptive non-local mean image denoising method. Firstly, the gray scale bar image is collected, and the non-local mean method is used to denoise it using different denoising intensity parameters to obtain the best image under different brightness. denoising intensity parameters; then use the linear interpolation method to calculate the optimal denoising intensity parameters corresponding to other brightness; finally use the optimal denoising intensity parameters corresponding to different brightnesses to denoise the image in the logarithmic domain, and the The image after digital domain denoising is subjected to exponential transformation to obtain the final image after denoising. The present invention overcomes the shortcomings of fixed denoising intensity parameters in the existing methods, and improves the denoising effect of the image; at the same time, processing in the logarithmic domain helps to increase the difference in pixel brightness in dark areas, reduce the difference in bright areas, and more It is beneficial to improve the denoising effect of the image.