Spectral super-resolution reconstruction method based on dynamic dictionary learning

Abstract

The present application relates to the technical field of image reconstruction, and more particularly to a spectral super-resolution reconstruction method based on dynamic dictionary learning. It comprises: building a DLTN network architecture, including a feature encoding unit, a spatial down-sampling unit, a multi-level dictionary learning and sparse coding unit, a feature decoding unit and a global feature fusion mechanism; obtaining a hyperspectral training dataset, preprocessing the input low-resolution RGB image, and inputting the network for training; inputting the low-resolution RGB image to be processed into the trained network, and sequentially processing it through feature encoding, spatial down-sampling, multi-level sparse coding and feature enhancement, multi-depth feature global fusion and feature decoding, and outputting the reconstructed hyperspectral image. The advantage is that dynamic dictionary learning and lightweight Transformer architecture are fused, feature sparse representation is realized through the DLTSC module, and the calculation cost is reduced; while ensuring the reconstruction accuracy, the model parameter quantity and the floating point operation frequency are significantly reduced.

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