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One Two One Network for Compression Artifacts Reduction in Remote Sensing

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Figure

Figure 1: Left: the SPIHT-compressed remotely sensed images with obvious blocking artifacts
Figure 2: The architecture of One-Two-One (OTO) Network. Two different CNN models are combined in a principledframework, the outputs of which are further processed based on a fusion network
Figure 3:The architecture of OTO(Linear)with a learned α to balance the two branchnetworks.
Figure 5: The architecture of multi-scaled OTO network (OTO RRR) in which ×1,× 12 and × 14 −scaled features areexploited
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