Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. It is generally performed early in the pipeline, usually before demosaicking, although studies swapping their order or even conducting them jointly have been proposed. With the advent of deep learning, the quality of denoising algorithms has steadily increased. Even so, modern neural networks still have a hard time adapting to new noise levels and scenes, which is indispensable for real-world applications. With those in mind, we propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data. We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking. We also integrate temporal trajectory prefiltering steps before each denoiser, which further improve texture reconstruction. The proposed method only requires an estimation of the noise model at the sensor, accurately adapts to any noise level, and is competitive with the state of the art, making it suitable for real-world videography.

翻译：降噪是将相机传感器捕获的数据转换为可供显示的图像或视频这一处理流程中的基础步骤之一。该步骤通常在流程早期执行，多在去马赛克操作之前，尽管已有研究提出交换两者顺序甚至联合执行的方案。随着深度学习的兴起，降噪算法的质量持续提升。即便如此，现代神经网络在面对新噪声水平与场景时仍难以灵活适应，而这在实际应用中不可或缺。基于此，我们提出一种基于自相似性的降噪方案，该方案对拜耳模式彩色滤波阵列视频数据同时加权处理去马赛克前与去马赛克后的降噪器。我们证明两者间的平衡能带来更优的图像质量，并通过实验发现较高噪声水平更受益于增强去马赛克前降噪的影响。我们还在每个降噪器前整合了时序轨迹预滤波步骤，从而进一步提升纹理重建效果。所提方法仅需估计传感器端的噪声模型，即可精确适应任意噪声水平，其性能与当前最优技术具有竞争力，适用于实际视频拍摄场景。