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Recently, Convolutional Neural Networks (CNNs) have shown promisingperformance in super-resolution (SR). However, these methods operate primarilyon Low Resolution (LR) inputs for memory efficiency but this limits, as wedemonstrate, their ability to (i) model high frequency information; and (ii)smoothly translate from LR to High Resolution (HR) space. To this end, wepropose a novel Incremental Residual Learning (IRL) framework to address thesementioned issues. In IRL, first we select a typical SR pre-trained network as amaster branch. Next we sequentially train and add residual branches to the mainbranch, where each residual branch is learned to model accumulated residuals ofall previous branches. We plug state of the art methods in IRL framework anddemonstrate consistent performance improvement on public benchmark datasets toset a new state of the art for SR at only approximately 20% increase intraining time.

Improving Super-Resolution Methods via Incremental Residual Learning