Signal-dependent noise estimation for a real-camera model via weight and shape constraints
Signal-dependent noise estimation for a real-camera model via weight and shape constraints
复制标题
通过重量和形状约束对真实相机模型进行信号相关噪声估计
DOI：
10.1109/tmm.2021.3056879
复制
发表时间：
--
影响因子：
7.3
通讯作者：
Zhang Xinpeng
中科院分区：
文献类型：
--
作者：
Yao Heng;Zou Mian;Qin Chuan;Zhang Xinpeng
大多数计算机视觉算法需要根据图像噪声水平调整参数。传统上，加性白色高斯噪声（AWGN）模型被广泛用于大多数噪声估计算法中;然而，这种假设在来自相机的噪声更复杂的真实的世界中不成立，并且假设信号相关噪声（SDN）模型更合适。在本文中，我们专注于SDN模型，同时考虑到非线性辐射校准内的实际相机，并提出了一种算法来有效地估计噪声水平函数（NLF），这是定义为相对于图像强度的噪声标准差。首先，输入图像被分成重叠的补丁，和噪声样本估计在线性变换域。然后，噪声样本的置信水平和相机响应函数的先验被用作NLF恢复的约束。最后，噪声样本和约束表示在一个凸优化问题。使用真实的和合成噪声图像的实验结果表明，该方法的优越性。此外，估计的NLF被纳入两个著名的去噪方案，非局部均值和BM 3D，并显示出显着的改善去噪SDN污染的图像。
Most computer vision algorithms require parameter adjustment according to the image noise level. Conventionally, the additive white Gaussian noise (AWGN) model is widely used in most noise estimation algorithms; however, this assumption does not hold in the real world where the noise from cameras is more complex, and it is more appropriate to assume the signal-dependent noise (SDN) model. In this paper, we focus on the SDN model while considering the nonlinear radiometric calibration inside an actual camera, and propose an algorithm to efficiently estimate the noise level function (NLF), which is defined as the noise standard deviation with respect to image intensity. First, the input image is divided into overlapping patches, and noise samples are estimated in the linear transform domain. The confidence levels of the noise samples and the prior of the camera response function are then employed as constraints for the recovery of the NLF. Finally, the noise samples and constraints are represented in a convex optimization problem. The experimental results using both real and synthetic noisy images demonstrate the superiority of the proposed method. In addition, the estimated NLFs are incorporated into two well-known denoising schemes, non-local means and BM3D, and shows significant improvements in denoising SDN-polluted images.