Papers › PIP: Positional-encoding Image Prior
PIP: Positional-encoding Image Prior
Nimrod Shabtay, Eli Schwartz, Raja Giryes
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/
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections