# Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer

arXiv: https://arxiv.org/abs/2206.03678 · HTML: https://mlsanity.com/paper/2206.03678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [Advancements and Trends in Ultra-High-Resolution Image Processing: An Overview](https://mlsanity.com/paper/2312.00250/) (2023)

## Abstract (arXiv)

Currently, transformer-based algorithms are making a splash in the domain of image deblurring. Their achievement depends on the self-attention mechanism with CNN stem to model long range dependencies between tokens. Unfortunately, this ear-pleasing pipeline introduces high computational complexity and makes it difficult to run an ultra-high-definition image on a single GPU in real time. To trade-off accuracy and efficiency, the input degraded image is computed cyclically over three dimensional (C, W, and H) signals without a self-attention mechanism. We term this deep network as Multi-scale Cubic-Mixer, which is acted on both the real and imaginary components after fast Fourier transform to estimate the Fourier coefficients and thus obtain a deblurred image. Furthermore, we combine the multi-scale cubic-mixer with a slicing strategy to generate high-quality results at a much lower computational cost. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring approaches on the several benchmarks and a new ultra-high-definition dataset in terms of accuracy and speed.