Patch-Based Residual Networks for Compressively Sensed Hyperspectral Images Restruction

Most traditional compressive sensing (CS) reconstruction methods suffer from the intensive computation caused by iterations. This paper aims at presenting a non-iterative algorithm to reconstruct hyperspectral images (HSI) from patch-based compressively sensed measurements. Our method contains two residual convolutional neural networks. One is reconstruction network for compressive sensing reconstruction and the other is deblocking network for removing the blocky effect, which is caused by patch-based sampling. The reconstruction network can efficiently reconstruct all the bands of HSI jointly, thus the spectral correlation is well preserved. In addition, the deblock performance is enhanced by combining more patches into a larger patch in the deblocking network. Experimental results verify that our method outperforms the state-of-the-art compressive sensing reconstruction methods with patch-based CS measurement.