Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models

• URL: http://arxiv.org/abs/2503.11073v1
• Date: Fri, 14 Mar 2025 04:33:59 GMT
• Title: Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models
• Authors: Hongyang Wei, Shuaizheng Liu, Chun Yuan, Lei Zhang,
• Abstract summary: We adapt the autoregressive multimodal model Lumina-mGPT into a robust Real-ISR model, namely PURE.<n>PURE Perceives and Understands the input low-quality image, then REstores its high-quality counterpart.<n> Experimental results demonstrate that PURE preserves image content while generating realistic details.
• Score: 33.76031793753807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: By leveraging the generative priors from pre-trained text-to-image diffusion models, significant progress has been made in real-world image super-resolution (Real-ISR). However, these methods tend to generate inaccurate and unnatural reconstructions in complex and/or heavily degraded scenes, primarily due to their limited perception and understanding capability of the input low-quality image. To address these limitations, we propose, for the first time to our knowledge, to adapt the pre-trained autoregressive multimodal model such as Lumina-mGPT into a robust Real-ISR model, namely PURE, which Perceives and Understands the input low-quality image, then REstores its high-quality counterpart. Specifically, we implement instruction tuning on Lumina-mGPT to perceive the image degradation level and the relationships between previously generated image tokens and the next token, understand the image content by generating image semantic descriptions, and consequently restore the image by generating high-quality image tokens autoregressively with the collected information. In addition, we reveal that the image token entropy reflects the image structure and present a entropy-based Top-k sampling strategy to optimize the local structure of the image during inference. Experimental results demonstrate that PURE preserves image content while generating realistic details, especially in complex scenes with multiple objects, showcasing the potential of autoregressive multimodal generative models for robust Real-ISR. The model and code will be available at https://github.com/nonwhy/PURE.

Related papers

• IntrinsiX: High-Quality PBR Generation using Image Priors [49.90007540430264]
  We introduce IntrinsiX, a novel method that generates high-quality intrinsic images from text description. In contrast to existing text-to-image models whose outputs contain baked-in scene lighting, our approach predicts physically-based rendering (PBR) maps.
  arXiv  Detail & Related papers  (2025-04-01T17:47:48Z)
• Visual Autoregressive Modeling for Image Super-Resolution [14.935662351654601]
  We propose a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction.<n>We collect large-scale data and design a training process to obtain robust generative priors.
  arXiv  Detail & Related papers  (2025-01-31T09:53:47Z)
• High-Resolution Image Synthesis via Next-Token Prediction [19.97037318862443]
  We introduce textbfD-JEPA$cdot$T2I, an autoregressive model based on continuous tokens to generate high-quality, photorealistic images at arbitrary resolutions, up to 4K.<n>For the first time, we achieve state-of-the-art high-resolution image synthesis via next-token prediction.
  arXiv  Detail & Related papers  (2024-11-22T09:08:58Z)
• SSP-IR: Semantic and Structure Priors for Diffusion-based Realistic Image Restoration [20.873676111265656]
  SSP-IR aims to fully exploit semantic and structure priors from low-quality images.<n>Our method outperforms other state-of-the-art methods overall on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-07-04T04:55:14Z)
• CoSeR: Bridging Image and Language for Cognitive Super-Resolution [74.24752388179992]
  We introduce the Cognitive Super-Resolution (CoSeR) framework, empowering SR models with the capacity to comprehend low-resolution images. We achieve this by marrying image appearance and language understanding to generate a cognitive embedding. To further improve image fidelity, we propose a novel condition injection scheme called "All-in-Attention"
  arXiv  Detail & Related papers  (2023-11-27T16:33:29Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• High-Quality Pluralistic Image Completion via Code Shared VQGAN [51.7805154545948]
  We present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed. Our framework is able to learn semantically-rich discrete codes efficiently and robustly, resulting in much better image reconstruction quality.
  arXiv  Detail & Related papers  (2022-04-05T01:47:35Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.