AbsoluteDegradation: A Physics-Inspired Synthetic Film-Degradation Pipeline and Archival Film Restoration Benchmark

Mikołaj Jastrzębski; Dawid Glinkowski; Dawid Zieliński; Daniel Borkowski; Wojciech Kozłowski; Kamil Adamczewski

AbsoluteDegradation: A Physics-Inspired Synthetic Film-Degradation Pipeline and Archival Film Restoration Benchmark

Mikołaj Jastrzębski, Dawid Glinkowski, Dawid Zieliński, Daniel Borkowski, Wojciech Kozłowski, Kamil Adamczewski
Wrocław University of Science and Technology

Conference on Neural Information Processing Systems (NeurIPS) 2026, Evaluations and Datasets Track

Paper (arXiv:2607.02131) · PDF · Code

Code and the benchmark dataset will be released by the end of October 2026.

Abstract

Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation benchmarks. Pristine versions of deteriorated footage are physically unrecoverable, requiring supervised methods to rely on synthetic data that often fail to capture the complex, temporally coherent nature of real film degradation. At the same time, existing real-world datasets are limited in scale, quality, and accessibility, hindering reliable evaluation and fair comparison across methods. We address both limitations with AbsoluteDegradation, a physics-inspired, modular pipeline for synthesizing realistic film degradations, and a new large-scale archival benchmark. The proposed pipeline models the analog-to-digital process as a structured composition of artifact families, incorporating signal-dependent grain, parametric scratches, and temporally coherent camera motion, enabling controlled generation of diverse degradation regimes. In parallel, we introduce a curated dataset of 81,576 high-resolution frames sourced from real archival footage, designed for consistent evaluation under real-world conditions. Together, these contributions provide a unified framework for training and benchmarking restoration models. Extensive experiments across multiple architectures show that models trained with AbsoluteDegradation generalize better to real-world footage, while the proposed benchmark reveals systematic failure modes of current methods. We hope this work establishes a foundation for reproducible and domain-authentic evaluation in archival film restoration.

Key results

Curated 81,576-frame benchmark from 30 public-domain films (1896–1918) sourced from the Library of Congress.
First synthesis pipeline to jointly model all 7 analog artifact families with temporal coherence and a severity curriculum.
Models trained on it generalize better to real footage and set SOTA, while exposing failure modes of prior methods.

Cite

@inproceedings{jastrzebski2026absolutedegradation,
  title     = {{AbsoluteDegradation}: A Physics-Inspired Synthetic Film-Degradation Pipeline and Archival Film Restoration Benchmark},
  author    = {Jastrz{\k{e}}bski, Miko{\l}aj and Glinkowski, Dawid and Zieli{\'n}ski, Dawid and Borkowski, Daniel and Koz{\l}owski, Wojciech and Adamczewski, Kamil},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}

Companion paper

DART: A Degradation-Aware Recurrent Transformer for Archival Film Restoration (ACCV 2026)