SAR image noise reduction processing method based on dictionary learning fusion

Abstract

The invention provides a fused SAR image noise reduction processing method based on dictionary learning. The method utilizes translation invariant non-subsampled contourlet transform filtering to overcome the defect that non-subsampled contourlet transform cannot realize translation invariance and eliminate the scratch effect for noise reduction by means of combination of the non-subsampled contourlet dictionary learning and K-SVD dictionary learning, and at the same time utilizes an adaptive K-SVD dictionary learning algorithm to perform noise reduction and continuously updates the dictionary atoms according to the characteristics of images, not only being able to restrain the image noise, but also being able to preferably reserve the important SAR image information, such as edges and texture; and the method further fuses the two noise reduction effects, so that the signal to noise ratio of the image is greatly improved after fusion of the images; the equivalent number of looks of the image is also improved; the edge and texture information is preferably reserved; the negative influence, such as scratches and darkening of the image contrast, does not appear; and therefore, the comprehensive quality for SAR image noise reduction processing is significantly improved.

Description

technical field [0001] The invention relates to the technical field of microwave remote sensing image processing, in particular to a SAR image noise reduction processing method based on dictionary learning fusion. Background technique [0002] Synthetic Aperture Radar (SAR) technology is a pulse radar technology that uses mobile radar mounted on satellites or aircraft to obtain radar target images in high-precision geographic areas. Synthetic Aperture Radar Auto Targets Recognition (SAR-ATR) has important application value in many geographic information analysis technology fields. [0003] Coherent speckle noise is an inherent characteristic of SAR images. The coherent speckles scattered randomly in SAR images will be mixed with smaller ground objects, which seriously affects the image quality and makes it difficult for the automatic interpretation of SAR images. Therefore, in SAR image processing, image noise suppression becomes the key, and it is also the technical basis ...

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Problems solved by technology

[0008] Aiming at the above-mentioned defects existing in the prior art, in order to solve the problem that the effect of SAR image denoising processing in the prior art is not ideal enough, the present invention provides a SAR image denoising processing method based on dictionary learning and fusion, which combines non-downstream Sampling contourlet dictionary learning and K-SVD dictionary learning form a multi-dictionary learning fusion noise reduction process for SAR images, which can greatly improve the signal-to-noise ratio of SAR images, and at the same time preserve the edge and texture information of SAR images well, thus Improving the quality of SAR image noise reduction processing

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