| Citation: | LI Ying-chao, ZHAO Zhe-hao, WANG Qi, LIU Jia-nan, SHI Hao-dong, FU Qiang, SUN Hong-yu. Polarization spectral image fusion method for hybrid backgrounds of ground objects[J]. Chinese Optics. doi: 10.37188/CO.2023-0185
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To address the issues of blurred edge details and poor contrast in multi-scale transform fused images obtained using remote sensing detection methods for mixed background features, an image fusion approach that combines the sparse representation of non-downsampled contour wavelet transform and a guided filter was utilised to enhance the quality and visual appearance of the fused images. This method involved several steps: Firstly, a multi-scale and multi-directional decomposition was performed on both spectral and polarimetric images using non-downsampled contour wavelet transform to isolate the feature information in each subband; secondly, the low-frequency subbands were fused using a sparse representation approach to minimize the loss of contrast in the fused image; additionally, the high-frequency subbands were fused through a bootstrap filter to enhance the detail information and the contours of the image; finally, the low-frequency and high-frequency fusion coefficients were inverted using non-downsampled contour wavelet inversion to generate the final fused image. Analysis indicates that this method has increased the contrast of the fused image by up to 54.5% relative to the original spectral image and by 15.4% compared to the polarimetric image. This has resulted in a fused image in which it is easier to distinguish objects in shadows within a mixed background. This method was used to fuse spectral and polarimetric images captured by a polarimetric spectral imager at different wavelengths, which resulted in true-colour reproduction. These true-colour restored images demonstrate that this fusion method retains environmental information within the mixed background while distinguishing the object from the background, effectively improving the image quality of polarization spectral remote sensing detection imaging. This approach enhances the integrity and authenticity of image information in polarization spectral remote sensing detection imaging, thereby expanding its application scope in remote sensing detection of complex environments and image recognition.
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