论文标题
一个新型的端到端网络，用于使用本地完全连接的层重建非规范采样图像数据
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected Layers
论文作者
论文摘要
四分之一采样和四分之三的采样是新型传感器概念，可实现高分辨率图像而无需增加像素的数量。这是通过非规范覆盖一个低分辨率传感器的每个像素的一部分来实现的，因此每个像素的传感器区域的一个象限或三个象限对光敏感。与使用低分辨率传感器和随后的更新采样相比，结合了正确设计的面膜和高质量的重建算法，可以实现更高的图像质量。对于后一种情况，可以使用超级超级分辨率网络（VDSR）等超级分辨率算法进一步增强图像质量。在本文中，我们提出了一个新型的端到端神经网络，以从非规范采样的传感器数据中重建高分辨率图像。该网络是本地完全连接的重建网络（LFCR）和标准VDSR网络的串联。总的来说，使用我们的新型神经网络布局，使用四分之三的采样传感器，与最先进的方法相比，Urban100数据集的PSNR图像质量可以增加2.96 dB。与使用VDSR的低分辨率传感器相比，获得了1.11 dB的增益。
Quarter sampling and three-quarter sampling are novel sensor concepts that enable the acquisition of higher resolution images without increasing the number of pixels. This is achieved by non-regularly covering parts of each pixel of a low-resolution sensor such that only one quadrant or three quadrants of the sensor area of each pixel is sensitive to light. Combining a properly designed mask and a high-quality reconstruction algorithm, a higher image quality can be achieved than using a low-resolution sensor and subsequent upsampling. For the latter case, the image quality can be further enhanced using super resolution algorithms such as the very deep super resolution network (VDSR). In this paper, we propose a novel end-to-end neural network to reconstruct high resolution images from non-regularly sampled sensor data. The network is a concatenation of a locally fully connected reconstruction network (LFCR) and a standard VDSR network. Altogether, using a three-quarter sampling sensor with our novel neural network layout, the image quality in terms of PSNR for the Urban100 dataset can be increased by 2.96 dB compared to the state-of-the-art approach. Compared to a low-resolution sensor with VDSR, a gain of 1.11 dB is achieved.
