Hierarchical Image Tokenization for Multi-Scale Image Super Resolution
We introduce a multi-scale Image Super Resolution (ISR) method building on recent advances in Visual Auto-Regressive (VAR) modeling. VAR models break image tokenization into additive, gradually increasing scales, using Residual Quantization (RQ), an approach that aligns perfectly with our target ISR task. Previous works taking advantage of this synergy suffer from two main shortcomings. First, due to the limitations in RQ, they only generate images at a predefined fixed scale, failing to map intermediate outputs to the corresponding image scales. They also rely on large backbones or a large corpus of annotated data to achieve better performance. To address both shortcomings, we introduce ...
| Authors | Isma Hadji, Enrique Sanchez, Adrian Bulat et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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