Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis elements. To this end, we introduce a novel basis prediction network that, given an input burst, predicts a set of global basis kernels -- shared within the image -- and the corresponding mixing coefficients -- which are specific to individual pixels. Compared to state-of-the-art techniques that output a large tensor of per-pixel spatiotemporal kernels, our formulation substantially reduces the dimensionality of the network output. This allows us to effectively exploit comparatively larger denoising kernels, achieving both significant quality improvements (over 1dB PSNR) and faster run-times over state-of-the-art methods.

翻译：图像的外壳在时间和空间上都显示出明显的自我差异。 这促使将内核作为一小组基础元素的线性组合来表示。 为此, 我们引入了一个新的基础预测网络, 在输入爆裂的情况下, 我们预测了一系列全球基础内核 -- -- 在图像中共享 -- -- 和相应的混合系数 -- -- 具体针对单个像素。 与产生大量全像粒子的超声波内核的最先进技术相比, 我们的配方大大降低了网络输出的维度。 这使我们能够有效地利用相对较大的去注内核, 实现显著的质量改进(1dB PSNR)和在最新方法上更快的运行时间。