An Improved Pulse-Coupled Neural Network Model for Pansharpening

Abstract

Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery

“…AWLP-H algorithm which showed an overall superiority among all methods is the instrument-optimized, haze-corrected version of the popular AWLP method. The good performance of this algorithm was also reported by Li et al (2020). As the important effect of haze on spatial and spectral quality of pan-sharpening algorithms was addressed by , haze correction can be a reason why the qualitative and quantitative results of AWLP-H could yield spatially/spectrally fine results.…”

“…The iterative regression-based approach for estimation of the injection coefficients at full resolution, proposed for the GLP-Reg-FS method, can estimate the injection coefficients in a way to minimize the spectral and spatial distortion of fusion product (Vivone et al, 2018). The good qualitative and quantitative results of this method was also reported by Li et al (2020). The high spectral quality of MMP can be attributed to its success in separating the spectral foreground and background, and opacity of foreground color (called alpha channel) from the MS image.…”

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

“…From the experimental results, we can conclude that the proposed fusion approach has superior performance compared to the state-of-the-art fusion methods. In future work, we will extend the algorithm to panchromatic and multispectral [ 43 , 44 , 45 , 46 , 47 , 48 ], hyperspectral and multispectral image fusion [ 49 , 50 ].…”

Brain Tumor Classification using the Probabilistic-Deformable Fuzzy system and RideNN

“…The final HHS image was reconstructed from the clustered HMS image with trained multi-branch BPNNs. Li, X et al [49] devised PPCNN model and adapted the model to performing pansharpening. Here, the PPCNN model comprises two external stimuli with standard PCNN that certifies it for performing fusion tasks.…”