无需摄像机标定的高动态范围图像栈噪声感知融合
Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration
摘要：
通过对相机噪声分布进行建模，可以从曝光堆栈获得高动态范围场景的辐射率的接近最优的重建。然后使用最大似然估计来估计潜在辐射。但这需要摄像机的良好校准的噪声模型，这在实践中很难获得。我们表明，可比方差的无偏估计，可以得到一个更简单的泊松噪声估计，这不需要的知识，相机特定的噪声参数。我们通过四种不同的相机来证明这一点，从智能手机相机到全画幅无反光镜相机。我们的实验结果是一致的模拟以及真实的图像，并在不同的相机设置。
英文摘要：
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.
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。
