  Deep convolutional neural network based image super-resolution (SR) models
have shown superior performance in recovering the underlying high resolution
(HR) images from low resolution (LR) images obtained from the predefined
downscaling methods. In this paper we propose a learned image downscaling
method based on content adaptive resampler (CAR) with consideration on the
upscaling process. The proposed resampler network generates content adaptive
image resampling kernels that are applied to the original HR input to generate
pixels 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 input
across the entire framework to adjust model parameters, the proposed framework
achieves a new state-of-the-art SR performance through upscaling guided image
resamplers which adaptively preserve detailed information that is essential to
the upscaling. Experimental results indicate that the quality of the generated
LR image is comparable to that of the traditional interpolation based method,
but the significant SR performance gain is achieved by deep SR models trained
jointly with the CAR model. The code is publicly available on: URL
https://github.com/sunwj/CAR.
