Learning Non-Uniform-Sampling for Ultra-High-Definition Image Enhancement

Ultra-high-definition (UHD) image enhancement is a challenging problem that aims to effectively and efficiently recover clean UHD images. To maintain efficiency, the straightforward approach is to downsample and perform most computations on low-resolution images. However, previous studies typically rely on the uniform and content-agnostic downsampling method that equally treats various regions regardless of their complexities, thus limiting the detail reconstruction in UHD image enhancement. To alleviate this issue, we propose a novel spatial-variant and invertible non-uniform downsampler that adaptively adjusts the sampling rate according to the richness of details. It magnifies important regions to preserve more information (e.g., sparse sampling points for sky, dense sampling points for buildings). Therefore, we propose a novel Non-uniform-Sampling Enhancement Network (NSEN) consisting of two core designs: 1) content-guided downsampling that extracts texture representation to guide the sampler to perform content-aware downsampling for producing detail-preserved low-resolution images; 2) invertible pixel-alignment which remaps the forward sampling process in an iterative manner to eliminate the deformations caused by the non-uniform downsampling, thus producing detail-rich clean UHD images. To demonstrate the superiority of our proposed model, we conduct extensive experiments on various UHD enhancement tasks. The results show that the proposed NSEN yields better performance against other state-of-the-art methods both visually and quantitatively.