论文标题
高动态范围图像堆栈的噪音了解没有相机校准
Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration
论文作者
论文摘要
可以通过对摄像机噪声分布进行建模来获得高动态范围场景的辐射的近乎最佳的重建。然后，使用最大似然估计来估算潜在的辐射。但这需要一个摄像机的噪声模型进行了良好的噪声模型，在实践中很难获得。我们表明，可以通过更简单的泊松噪声估计器获得对可比方差的无偏估计，这不需要了解摄像机特定的噪声参数。我们对四个不同的摄像机进行经验证明了这一点，从智能手机相机到完整的无镜相机。对于模拟和真实图像以及在不同的相机设置中，我们的实验结果是一致的。
A near-optimal reconstruction of the radiance of a High Dynamic Range scene from an exposure stack can be obtained by modeling the camera noise distribution. The latent radiance is then estimated using Maximum Likelihood Estimation. But this requires a well-calibrated noise model of the camera, which is difficult to obtain in practice. We show that an unbiased estimation of comparable variance can be obtained with a simpler Poisson noise estimator, which does not require the knowledge of camera-specific noise parameters. We demonstrate this empirically for four different cameras, ranging from a smartphone camera to a full-frame mirrorless camera. Our experimental results are consistent for simulated as well as real images, and across different camera settings.
