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Deep convolutional neural network based image super-resolution (SR) modelshave shown superior performance in recovering the underlying high resolution(HR) images from low resolution (LR) images obtained from the predefineddownscaling methods. In this paper we propose a learned image downscalingmethod based on content adaptive resampler (CAR) with consideration on theupscaling process. The proposed resampler network generates content adaptiveimage resampling kernels that are applied to the original HR input to generatepixels on the downscaled image. Moreover, a differentiable upscaling (SR)module is employed to upscale the LR result into its underlying HR counterpart.By back-propagating the reconstruction error down to the original HR inputacross the entire framework to adjust model parameters, the proposed frameworkachieves a new state-of-the-art SR performance through upscaling guided imageresamplers which adaptively preserve detailed information that is essential tothe upscaling. Experimental results indicate that the quality of the generatedLR image is comparable to that of the traditional interpolation based method,but the significant SR performance gain is achieved by deep SR models trainedjointly with the CAR model. The code is publicly available on: URLhttps://github.com/sunwj/CAR.

Learned Image Downscaling for Upscaling Using Content Adaptive Resampler