迈向无摩尔纹且保细节的去马赛克
Toward Moiré-Free and Detail-Preserving Demosaicking
AI总结：
针对去马赛克中摩尔纹与细节保留的冲突，提出解耦标准卷积为光谱与空间特征聚合的新方法，在相似或更低计算成本下显著减轻伪影，适用于联合去噪与独立去马赛克任务。
摘要：
3D卷积通常被去马赛克神经模型所采用，这与解决其他图像恢复问题的方式相同。出乎意料的是，我们表明3D卷积隐式地阻碍了RGB颜色光谱交换互补信息，导致局部空间高频分量的光谱不一致推断。因此，浅层3D卷积网络遭受摩尔纹伪影，而深层3D卷积则导致过度平滑。我们分析了去马赛克与其他预测可用像素之间丢失像素的问题（例如超分辨率重建）之间的根本差异，并提出了无摩尔纹与细节保留之间冲突的根本原因。从新的视角出发，我们的工作将常见的标准卷积过程解耦为光谱和空间特征聚合，这允许在光谱维度上加强全局通信，同时在空间维度上尊重局部对比度。我们将我们的去马赛克模型应用于两个任务：联合去马赛克-去噪和独立去马赛克。在这两个应用中，我们的模型在相似或更低的计算成本下，大幅减轻了摩尔纹和过度平滑等伪影，这已通过多种评估得到验证。源代码将在论文发表时随附发布。
英文摘要：
3D convolutions are commonly employed by demosaicking neural models, in the same way as solving other image restoration problems. Counter-intuitively, we show that 3D convolutions implicitly impede the RGB color spectra from exchanging complementary information, resulting in spectral-inconsistent inference of the local spatial high frequency components. As a consequence, shallow 3D convolution networks suffer the Moiré artifacts, but deep 3D convolutions cause over-smoothness. We analyze the fundamental difference between demosaicking and other problems that predict lost pixels between available ones (e.g., super-resolution reconstruction), and present the underlying reasons for the confliction between Moiré-free and detail-preserving. From the new perspective, our work decouples the common standard convolution procedure to spectral and spatial feature aggregations, which allow strengthening global communication in the spectral dimension while respecting local contrast in the spatial dimension. We apply our demosaicking model to two tasks: Joint Demosaicking-Denoising and Independently Demosaicking. In both applications, our model substantially alleviates artifacts such as Moiré and over-smoothness at similar or lower computational cost to currently top-performing models, as validated by diverse evaluations. Source code will be released along with paper publication.
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。
