This invention relates to a
Gaussian super-resolution
reconstruction method based on physically consistent sparsity and frequency awareness, comprising: performing
feature extraction and saliency prediction on the
spacecraft image to be processed to obtain a multi-scale deep feature
tensor and a foreground saliency
mask; using the foreground saliency
mask to perform spatial feature modulation on the multi-scale deep feature
tensor to obtain a modulated multi-scale deep feature
tensor; decoding the
feature vector corresponding to each spatial position of the modulated multi-scale deep feature tensor to predict the corresponding
Gaussian kernel parameters; based on the foreground saliency
mask, performing a physically consistent hard gating operation on the
opacity of all
Gaussian kernel parameters to generate a sparsified set of Gaussian kernel parameters; and based on the sparsified set of Gaussian kernel parameters, obtaining the final super-resolution result image through differentiable Gaussian
sputtering. This method can obtain high-quality arbitrary-scale super-resolution reconstruction results while meeting the high-performance requirements of spaceborne edge devices.