用于快速图像超分辨率的混合MLP-PNN架构
A hybrid MLP-PNN architecture for fast image superresolution
摘要：
图像超分辨率方法处理场景的输入图像序列以获得具有增加的分辨率的静止图像。解决这个问题的经典方法涉及复杂的迭代最小化过程，通常具有很高的计算成本。本文提出了一种新的超分辨率算法，使计算机负载大幅减少。首先，使用概率神经网络架构来执行图像序列数据的散点插值。网络的核函数是最佳的确定这个问题的多层感知器上训练的合成数据。定量分析了网络参数对序列噪声水平的依赖关系。对该超采样图像进行空间滤波以校正有限像素尺寸效应，从而产生最终的高分辨率估计。一个真正的室外序列的结果，显示所提出的方法的质量。
英文摘要：
Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First, a probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data. The network kernel function is optimally determined for this problem by a multi-layer perceptron trained on synthetic data. Network parameters dependence on sequence noise level is quantitatively analyzed. This super-sampled image is spatially filtered to correct finite pixel size effects, to yield the final high-resolution estimate. Results on a real outdoor sequence are presented, showing the quality of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
