Learning image compressed sensing with sub-pixel convolutional generative adversarial network

Highlights：

• The proposed SCGAN learns an explicit mapping from the compressed measurement to the reconstruction in an adversarial manner, thereby the reconstruction to any testing sample can be obtained by simply feeding the measurement vector into the well-trained generator, improving significantly the reconstruction quality and speeding up the CS reconstruction procedure greatly.

• Our generator network uses multiple sub-pixel convolutions to progressively upscale the dimension of feature maps until reaching the dimension of the original image. The sub-pixel convolutions can extract more feature maps for resolution upscaling and thus promote the reconstruction quality.

• We design a compound loss function, consisting of CS task-oriented reconstruction loss, measurement loss term and Wasserstein adversarial loss term, to encourage the output of generator to have a similar statistical distribution as the real images.