论文标题
基于可靠性的网格图像重建
Reliability-based Mesh-to-Grid Image Reconstruction
论文作者
论文摘要
本文提出了一种新的方法，用于重建来自位于非全能位置的样品，称为网格。对于许多图像处理应用程序，例如多摄像机系统中的超分辨率，翘曲或虚拟视图生成，这是一个常见的情况。所提出的方法依赖于一组初始估计，后来由新的基于可靠性的内容自适应框架来完善，该框架采用了denosing来减少重建误差。计算初始估计的可靠性，因此将更强的降解应用于较不可靠的估计值。相对于初始估计，提出的技术可以将重建质量提高超过2 dB（就PSNR而言），并且它的表现优于最先进的基于DeNoisis的细化，高达0.7 dB。
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.
