Guided upsampling is an effective approach for accelerating high-resolution image processing. In this paper, we propose a simple yet effective guided upsampling method. Each pixel in the high-resolution image is represented as a linear interpolation of two low-resolution pixels, whose indices and weights are optimized to minimize the upsampling error. The downsampling can be jointly optimized in order to prevent missing small isolated regions. Our method can be derived from the color line model and local color transformations. Compared to previous methods, our method can better preserve detail effects while suppressing artifacts such as bleeding and blurring. It is efficient, easy to implement, and free of sensitive parameters. We evaluate the proposed method with a wide range of image operators, and show its advantages through quantitative and qualitative analysis. We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing. In particular, for interactive editing, the joint optimization can be precomputed, thus allowing for instant feedback without hardware acceleration.

翻译：引导式上采样是加速高分辨率图像处理的有效方法。本文提出了一种简单而高效的引导式上采样方法。高分辨率图像中的每个像素被表示为两个低分辨率像素的线性插值，其索引和权重经过优化以最小化上采样误差。下采样过程可被联合优化，以防止遗漏细小孤立区域。该方法可基于颜色线模型和局部颜色变换推导得出。与现有方法相比，本方法能更好地保留细节效果，同时抑制颜色渗漏和模糊等伪影。它高效、易于实现且无需敏感参数。我们通过多种图像算子对该方法进行评估，并通过定量与定性分析展示其优势。我们证明了该方法在交互式图像编辑和实时高分辨率视频处理中的优越性。特别地，针对交互式编辑场景，联合优化可预计算，从而在无需硬件加速的情况下实现即时反馈。