This paper presents a novel method for the reconstruction of images from samples located at non-integer positions, called mesh. This is a common scenario for many image processing applications, such as super-resolution, warping or virtual view generation in multi-camera systems. The proposed method relies on a set of initial estimates that are later refined by a new reliability-based content-adaptive framework that employs denoising in order to reduce the reconstruction error. The reliability of the initial estimate is computed so stronger denoising is applied to less reliable estimates. The proposed technique can improve the reconstruction quality by more than 2 dB (in terms of PSNR) with respect to the initial estimate and it outperforms the state-of-the-art denoising-based refinement by up to 0.7 dB.
翻译:本文介绍了从位于非整数位置的样本中重建图像的新方法,称为网状图。这是许多图像处理应用的常见方案,如超分辨率、扭曲或多相机系统中的虚拟视图生成。拟议方法依赖一套初步估计,这些初步估计后来通过一个新的基于可靠性的内容调适框架加以完善,该框架采用拆分来减少重建错误。初步估计的可靠性被计算得如此可靠,因此,对不可靠的估计应用了更强的除雷法。拟议的技术可以在初步估计方面提高2dB(PSNR)以上的重建质量,并且比目前最先进的除雷法改进了0.7 dB。