We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled multi-frame fusion provided by the classical MAP formulation. We validate our approach through comprehensive experiments on burst denoising and burst super-resolution datasets. Our approach sets a new state-of-the-art for both tasks, demonstrating the generality and effectiveness of the proposed formulation.
翻译:我们建议对多框架图像恢复任务中常用的最大事后配方进行深刻的重新校正。我们的方法是通过引入一个学到的错误度量和目标图像的潜在代表来得出的,从而将MAP的目标转换为深地貌空间。深度的重新校正使我们能够直接模拟潜空的图像形成过程,并将先学到的图像纳入预测。我们的方法因此利用了深层次学习的优势,同时也得益于经典MAP的配方提供的原则性多框架融合。我们通过对爆炸性分泌和爆破超分辨率数据集的全面实验来验证我们的方法。我们的方法为这两项任务制定了新的最新设计,显示了拟议配方的普遍性和有效性。