Remote Sensing Image Reconstruction Method Based on Parameter Adaptive Dual-Channel Pulse-Coupled Neural Network to Optimize Multiscale Decomposition

Abstract

Existing image processing methods usually divide image denoising and image fusion into two directions for research, and even the best current image denoising methods such as DnCNN can cause information loss during image processing, and the image fusion method mainly considers the fusion between multiple source images to complement the image information, but does not take into account the degradation of the fusion quality due to the noise in the source images. Therefore, aiming at the problem that various existing image denoising methods cannot reduce noise efficiently in complex noise situations while performing multi-source image fusion, preserving texture details, highlighting edge contour structures, and enriching image energy, a method of reconstructing remote sensing images by simplified adaptive dual-channel PCNN (Dual-PCNN) fusion in the NSCT transform domain is proposed to unify image noise reduction and image fusion under the same framework to obtain a noisy image information reconstruction method, which completes the complementary advantages between two image processing methods. Firstly, the impulse noise in the image is removed using IMFLED filtering, and then the Gaussian noise is processed by DnCNN and FFDNet respectively, and the results are imported into simplified parametric adaptive dual-channel PCNN to fuse the preprocessed images respectively. Then the DnCNN image and the preprocessed image are decomposed by NSCT to obtain each low-frequency sub-band and high-frequency sub-band. The low-frequency sub-band is stimulated with detail using the guided filter, and the high-frequency sub-band is enhanced by separating the detail layer through the guided filter to obtain the energy-detail-enhanced high-frequency sub-band. Finally, the corresponding sub-bands are fused into the simplified parametric adaptive dual-channel PCNN respectively, and the fused sub-band coefficients are reconstructed by NSCT inversion to obtain the final reconstructed remote sensing image. The experiments on grayscale images and remote sensing images show that this method achieves excellent results in both visual subjective and quantitative index evaluation, and the reconstructed images perform well in texture details, contour structure, and energy enrichment. After the information reconstruction of remote sensing images, the quality and resolution of remote sensing images are effectively improved, so that terrain information, landform features, and structural features can be extracted more accurately, which are widely used in remote sensing and geographic information fields such as landform analysis, agricultural monitoring, building inspection and environmental protection.

Keywords

• Image reconstruction
• non-subsampled contourlet transform (NSCT)
• pulse-coupled neural network (PCNN)
• convolutional neural network (CNN)
• guided filter