Low-Light Image Enhancement with Wavelet-based Diffusion Models

• URL: http://arxiv.org/abs/2306.00306v3
• Date: Mon, 25 Sep 2023 04:57:23 GMT
• Title: Low-Light Image Enhancement with Wavelet-based Diffusion Models
• Authors: Hai Jiang, Ao Luo, Songchen Han, Haoqiang Fan, Shuaicheng Liu
• Abstract summary: 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.
• Score: 50.632343822790006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method. Code is available at https://github.com/JianghaiSCU/Diffusion-Low-Light.

Related papers

• A Wavelet Diffusion GAN for Image Super-Resolution [7.986370916847687]
  Diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation. However, their real-time feasibility is hindered by slow training and inference speeds. This study proposes a wavelet-based conditional Diffusion GAN scheme for Single-Image Super-Resolution.
  arXiv  Detail & Related papers  (2024-10-23T15:34:06Z)
• Distilling Diffusion Models into Conditional GANs [90.76040478677609]
  We distill a complex multistep diffusion model into a single-step conditional GAN student model. For efficient regression loss, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space. We demonstrate that our one-step generator outperforms cutting-edge one-step diffusion distillation models.
  arXiv  Detail & Related papers  (2024-05-09T17:59:40Z)
• Lossy Image Compression with Foundation Diffusion Models [10.407650300093923]
  In this work we formulate the removal of quantization error as a denoising task, using diffusion to recover lost information in the transmitted image latent. Our approach allows us to perform less than 10% of the full diffusion generative process and requires no architectural changes to the diffusion model.
  arXiv  Detail & Related papers  (2024-04-12T16:23:42Z)
• Efficient Diffusion Model for Image Restoration by Residual Shifting [63.02725947015132]
  This study proposes a novel and efficient diffusion model for image restoration. Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration. Our method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks.
  arXiv  Detail & Related papers  (2024-03-12T05:06:07Z)
• Global Structure-Aware Diffusion Process for Low-Light Image Enhancement [64.69154776202694]
  This paper studies a diffusion-based framework to address the low-light image enhancement problem. We advocate for the regularization of its inherent ODE-trajectory. Experimental evaluations reveal that the proposed framework attains distinguished performance in low-light enhancement.
  arXiv  Detail & Related papers  (2023-10-26T17:01:52Z)
• Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution [0.0]
  Current methods assume the degradation to be known and provide impressive results in terms of restoration and diversity. In this work, we leverage the efficiency of those models to jointly estimate the restored image and unknown parameters of the kernel model. Our method alternates between approximating the expected log-likelihood of the problem using samples drawn from a diffusion model and a step to estimate unknown model parameters.
  arXiv  Detail & Related papers  (2023-09-01T06:47:13Z)
• ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting [70.83632337581034]
  Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed. We propose a novel and efficient diffusion model for SR that significantly reduces the number of diffusion steps. Our method constructs a Markov chain that transfers between the high-resolution image and the low-resolution image by shifting the residual.
  arXiv  Detail & Related papers  (2023-07-23T15:10:02Z)
• ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
  We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
  arXiv  Detail & Related papers  (2023-07-03T06:49:04Z)

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.