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

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

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

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