论文标题
通过单个潜在变量和完全连接的神经网络代表相机响应功能
Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network
论文作者
论文摘要
建模从场景辐照度到图像强度的映射对于许多计算机视觉任务至关重要。这样的映射称为相机响应。大多数数码相机都使用非线性函数来映射辐照度，如传感器所测量的图像强度用于记录照片。响应的建模对于非线性校准是必需的。在本文中，提出了一种使用单个潜在变量且完全连接的神经网络的新的高性能摄像机响应模型。该模型是使用无监督的学习与现实世界（示例）摄像头响应上的自动编码器一起生产的。然后，使用神经体系结构搜索来找到最佳的神经网络体系结构。引入了一种潜在的分布学习方法来限制潜在分布。所提出的模型在许多基准测试中实现了最新的CRF表示精度，但由于简单但有效的模型表示，在执行相机响应校准期间执行最大似然估计时，几乎是当前模型的两倍。
Modelling the mapping from scene irradiance to image intensity is essential for many computer vision tasks. Such mapping is known as the camera response. Most digital cameras use a nonlinear function to map irradiance, as measured by the sensor to an image intensity used to record the photograph. Modelling of the response is necessary for the nonlinear calibration. In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed. The model is produced using unsupervised learning with an autoencoder on real-world (example) camera responses. Neural architecture searching is then used to find the optimal neural network architecture. A latent distribution learning approach was introduced to constrain the latent distribution. The proposed model achieved state-of-the-art CRF representation accuracy in a number of benchmark tests, but is almost twice as fast as the best current models when performing the maximum likelihood estimation during camera response calibration due to the simple yet efficient model representation.
