Learned Scale-Arbitrary Image Downscaling for Non-Learnable Upscaling

In this letter, we propose a learned scale-arbitrary image downscaling method to downscale high-resolution (HR) images to a target low-resolution (LR) where it could be well upscaled by traditional simple upscaling methods. Specifically, the scale information is first fused into the feature extraction process of the input HR image by employing our proposed scale-adaptive feature enhancement module (FEM). Then the proposed scale-arbitrary feature resampler (SAFR) adaptively generates sampling weights which are applied to the resampled candidate feature maps to produce the downscaled LR image. Experimental results demonstrate that when compared to the existing learned image downscaling method for non-learnable upscaling, the reconstructed HR images upscaled by our proposed method receive better quality..