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 method that combines the sparse representation of non-downsampled contour wavelet transform and a guided filter is utilised to enhance the quality and visual appearance of the fused images. This method involves several steps: firstly, a multi-scale and multi-directional decomposition is 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 are fused using a sparse representation to minimize the loss of contrast in the fused image; additionally, the high-frequency subbands are 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 are inverted using non-downsampled contour wavelet inversion to generate the final fused image. Analysis results indicate that the contrast of the fused image is improved by up to 54.5% and 15.4% respectively compared to the original spectral image and the polarimetric image, making it easier to distinguish objects in shadows under a mixed background. This method is 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 method can enhance 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.