论文标题
PIP：位置编码图像先验
PIP: Positional-encoding Image Prior
论文作者
论文摘要
在深图像先验（DIP）中，安装了卷积神经网络（CNN）将潜在空间映射到降级（例如嘈杂）图像中，但在此过程中，学会了重建干净的图像。这种现象归因于CNN的内部图像优点。我们重新访问了DIP框架，从神经隐含表示的角度进行了检查。通过这种观点的激励，我们用傅立叶功能（位置编码）代替了随机或学到的潜在。我们证明，由于傅立叶功能属性，我们可以用简单的像素级MLP替换卷积层。 We name this scheme ``Positional Encoding Image Prior" (PIP) and exhibit that it performs very similarly to DIP on various image-reconstruction tasks with much less parameters required. Additionally, we demonstrate that PIP can be easily extended to videos, where 3D-DIP struggles and suffers from instability. Code and additional examples for all tasks, including videos, are available on the project page https://nimrodshabtay.github.io/pip/
In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e.g. noisy) image but in the process learns to reconstruct the clean image. This phenomenon is attributed to CNN's internal image-prior. We revisit the DIP framework, examining it from the perspective of a neural implicit representation. Motivated by this perspective, we replace the random or learned latent with Fourier-Features (Positional Encoding). We show that thanks to the Fourier features properties, we can replace the convolution layers with simple pixel-level MLPs. We name this scheme ``Positional Encoding Image Prior" (PIP) and exhibit that it performs very similarly to DIP on various image-reconstruction tasks with much less parameters required. Additionally, we demonstrate that PIP can be easily extended to videos, where 3D-DIP struggles and suffers from instability. Code and additional examples for all tasks, including videos, are available on the project page https://nimrodshabtay.github.io/PIP/
