TTTIR: Unlocking Instance-Specific State Evolution via Test-Time Training for Image Restoration

Abstract:Image restoration is inherently challenging due to the diverse and highly input-dependent nature of real-world degradations. While recent architectures like Transformers and state-space models have advanced the field, they predominantly rely on static, globally shared parameters, which struggle to fully accommodate instance-specific degradation patterns. Test-Time Training (TTT) offers a promising paradigm for generating data-dependent operators, yet its standard self-supervised inner loop lacks the explicit guidance required to transition degraded features toward clean structures. To address this, we propose TTTIR, a novel framework that reformulates image restoration as an instance-specific state evolution process. Specifically, we design Progressive State Sequence Generation (PSSG) to construct complementary spatial-frequency target states (defining what to recover), and State Transition Evolution (STE) to adapt lightweight transition operators via a restoration-oriented TTT inner loop (determining how the features should evolve). Extensive experiments demonstrate that TTTIR consistently outperforms state-of-the-art models across multiple image restoration benchmarks, achieving dynamic instance-specific recovery with favorable computational scalability. The code is available at this https URL.

TL;DR: TTTIR improves image restoration by framing it as an instance-specific state evolution process. Powered by Test-Time Training (TTT), it dynamically adapts operators to handle real-world degradations, outperforming state-of-the-art models with scalable efficiency. 11 pages, 6 figures, 6 tables

Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)

arXiv:2609.26151 [eess.IV]

(or arXiv:2609.26151v1 [eess.IV] for this version)

https://doi.org/10.48550/arXiv.2609.26151 arXiv-issued DOI via DataCite (pending registration)