One-step Generative Diffusion for Realistic Extreme Image Rescaling

• URL: http://arxiv.org/abs/2408.09151v2
• Date: Tue, 19 Nov 2024 14:41:13 GMT
• Title: One-step Generative Diffusion for Realistic Extreme Image Rescaling
• Authors: Ce Wang, Zhenyu Hu, Wanjie Sun, Zhenzhong Chen,
• Abstract summary: We propose a novel framework called One-Step Image Rescaling Diffusion (OSIRDiff) for extreme image rescaling. OSIRDiff performs rescaling operations in the latent space of a pre-trained autoencoder. It effectively leverages powerful natural image priors learned by a pre-trained text-to-image diffusion model.
• Score: 47.89362819768323
• License:
• Abstract: Image rescaling aims to learn the optimal low-resolution (LR) image that can be accurately reconstructed to its original high-resolution (HR) counterpart, providing an efficient image processing and storage method for ultra-high definition media. However, extreme downscaling factors pose significant challenges to the upscaling process due to its highly ill-posed nature, causing existing image rescaling methods to struggle in generating semantically correct structures and perceptual friendly textures. In this work, we propose a novel framework called One-Step Image Rescaling Diffusion (OSIRDiff) for extreme image rescaling, which performs rescaling operations in the latent space of a pre-trained autoencoder and effectively leverages powerful natural image priors learned by a pre-trained text-to-image diffusion model. Specifically, OSIRDiff adopts a pseudo-invertible module to establish the bidirectional mapping between the latent features of the HR image and the target-sized LR image. Then, the rescaled features are refined by a pre-trained diffusion model to generate more faithful and visually pleasing details. The entire model is end-to-end trained to enable the diffusion priors to guide the rescaling process. Considering the spatially non-uniform reconstruction quality of the rescaled latent features, we propose a novel time-step alignment strategy, which can adaptively determine the generative strength of the diffusion model based on the degree of latent reconstruction errors. Extensive experiments demonstrate the superiority of OSIRDiff over previous methods in both quantitative and qualitative evaluations.

Related papers

• Effective Diffusion Transformer Architecture for Image Super-Resolution [63.254644431016345]
  We design an effective diffusion transformer for image super-resolution (DiT-SR) In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks. We analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module.
  arXiv  Detail & Related papers  (2024-09-29T07:14:16Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• CasSR: Activating Image Power for Real-World Image Super-Resolution [24.152495730507823]
  Cascaded diffusion for Super-Resolution, CasSR, is a novel method designed to produce highly detailed and realistic images. We develop a cascaded controllable diffusion model that aims to optimize the extraction of information from low-resolution images.
  arXiv  Detail & Related papers  (2024-03-18T03:59:43Z)
• Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction [4.227116189483428]
  This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation framework. It includes the low-quality image generation in latent space and the high-quality image generation in pixel space. It minimizes computational costs by moving some inference steps from pixel space to latent space.
  arXiv  Detail & Related papers  (2024-03-14T12:58:28Z)
• JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement [69.6035373784027]
  Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. Previous methods may neglect the importance of a sufficient formulation of task-specific condition strategy. We propose JoReS-Diff, a novel approach that incorporates Retinex- and semantic-based priors as the additional pre-processing condition.
  arXiv  Detail & Related papers  (2023-12-20T08:05:57Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)
• A Unified Conditional Framework for Diffusion-based Image Restoration [39.418415473235235]
  We present a unified conditional framework based on diffusion models for image restoration. We leverage a lightweight UNet to predict initial guidance and the diffusion model to learn the residual of the guidance. To handle high-resolution images, we propose a simple yet effective inter-step patch-splitting strategy.
  arXiv  Detail & Related papers  (2023-05-31T17:22:24Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Invertible Image Rescaling [118.2653765756915]
  We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images. We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
  arXiv  Detail & Related papers  (2020-05-12T09:55:53Z)

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.