# Image Deblurring by Exploring In-depth Properties of Transformer arXiv: https://arxiv.org/abs/2303.15198 ยท HTML: https://mlsanity.com/paper/2303.15198/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 48 of its 71 references that are in the map. - First posted on arXiv 24 Mar 2023. ## Builds on (most cited first, 30 of 48) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021) - [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016) - [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016) - [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021) - [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017) - [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020) - [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021) - [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021) - [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016) - [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022) - [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021) - [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017) - [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019) - [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016) - [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018) - [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021) - [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021) - [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020) - [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021) - [Splicing ViT Features for Semantic Appearance Transfer](https://mlsanity.com/paper/2201.00424/) (2022) - [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023) - [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018) - [Deblurring by Realistic Blurring](https://mlsanity.com/paper/2004.01860/) (2020) - [Human-Aware Motion Deblurring](https://mlsanity.com/paper/2001.06816/) (2020) - [Defocus Deblurring Using Dual-Pixel Data](https://mlsanity.com/paper/2005.00305/) (2020) - [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021) - [Single Image Reflection Separation with Perceptual Losses](https://mlsanity.com/paper/1806.05376/) (2018) ## Built on by (most cited first, 10 of 10) - [MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training](https://mlsanity.com/paper/2404.11016/) (2024) - [GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.09588/) (2024) - [Deep learning in motion deblurring: current status, benchmarks and future prospects](https://mlsanity.com/paper/2401.05055/) (2024) - [Toward Flare-Free Images: A Survey](https://mlsanity.com/paper/2310.14354/) (2023) - [Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression](https://mlsanity.com/paper/2401.14007/) (2024) - [Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models](https://mlsanity.com/paper/2501.12604/) (2025) - [HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models](https://mlsanity.com/paper/2401.05870/) (2024) - [Learning Dual Transformers for All-In-One Image Restoration from a Frequency Perspective](https://mlsanity.com/paper/2407.01636/) (2024) - [RED: Robust Event-Guided Motion Deblurring with Modality-Specific Disentanglement](https://mlsanity.com/paper/2509.05554/) (2025) - [Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection](https://mlsanity.com/paper/2605.21964/) (2026) ## Abstract (arXiv) Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of transformer properties, we introduce the pretrained transformers to address this problem. In particular, we leverage deep features extracted from a pretrained vision transformer (ViT) to encourage recovered images to be sharp without sacrificing the performance measured by the quantitative metrics. The pretrained transformer can capture the global topological relations (i.e., self-similarity) of image, and we observe that the captured topological relations about the sharp image will change when blur occurs. By comparing the transformer features between recovered image and target one, the pretrained transformer provides high-resolution blur-sensitive semantic information, which is critical in measuring the sharpness of the deblurred image. On the basis of the advantages, we present two types of novel perceptual losses to guide image deblurring. One regards the features as vectors and computes the discrepancy between representations extracted from recovered image and target one in Euclidean space. The other type considers the features extracted from an image as a distribution and compares the distribution discrepancy between recovered image and target one. We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks.