Efficient Diffusion as Low Light Enhancer

• URL: http://arxiv.org/abs/2410.12346v2
• Date: Thu, 21 Nov 2024 08:20:04 GMT
• Title: Efficient Diffusion as Low Light Enhancer
• Authors: Guanzhou Lan, Qianli Ma, Yuqi Yang, Zhigang Wang, Dong Wang, Xuelong Li, Bin Zhao,
• Abstract summary: Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
• Score: 63.789138528062225
• License:
• Abstract: The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation, highlighting the trade-off between performance and efficiency. In this paper, we identify two primary factors contributing to performance degradation: fitting errors and the inference gap. Our key insight is that fitting errors can be mitigated by linearly extrapolating the incorrect score functions, while the inference gap can be reduced by shifting the Gaussian flow to a reflectance-aware residual space. Based on the above insights, we design Reflectance-Aware Trajectory Refinement (RATR) module, a simple yet effective module to refine the teacher trajectory using the reflectance component of images. Following this, we introduce \textbf{Re}flectance-aware \textbf{D}iffusion with \textbf{Di}stilled \textbf{T}rajectory (\textbf{ReDDiT}), an efficient and flexible distillation framework tailored for LLIE. Our framework achieves comparable performance to previous diffusion-based methods with redundant steps in just 2 steps while establishing new state-of-the-art (SOTA) results with 8 or 4 steps. Comprehensive experimental evaluations on 10 benchmark datasets validate the effectiveness of our method, consistently outperforming existing SOTA methods.

Related papers

• Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration [31.50005609235654]
  This study rethinks the current landscape of training-free token reduction research. We propose a unified ''filter-correlate-compress'' paradigm that decomposes the token reduction into three distinct stages. Experimental results across 10 benchmarks indicate that our methods can achieve up to an 82.4% reduction in FLOPs.
  arXiv  Detail & Related papers  (2024-11-26T18:53:51Z)
• One Step Diffusion-based Super-Resolution with Time-Aware Distillation [60.262651082672235]
  Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. Recent techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. We propose a time-aware diffusion distillation method, named TAD-SR, to accomplish effective and efficient image super-resolution.
  arXiv  Detail & Related papers  (2024-08-14T11:47:22Z)
• Learning Point Spread Function Invertibility Assessment for Image Deconvolution [14.062542012968313]
  We propose a metric that employs a non-linear approach to learn the invertibility of an arbitrary PSF using a neural network. A lower discrepancy between the mapped PSF and a unit impulse indicates a higher likelihood of successful inversion by a DL network.
  arXiv  Detail & Related papers  (2024-05-25T20:00:27Z)
• LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights [2.8461446020965435]
  We introduce LD-Pruner, a novel performance-preserving structured pruning method for compressing Latent Diffusion Models. We demonstrate the effectiveness of our approach on three different tasks: text-to-image (T2I) generation, Unconditional Image Generation (UIG) and Unconditional Audio Generation (UAG)
  arXiv  Detail & Related papers  (2024-04-18T06:35:37Z)
• 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)
• SinSR: Diffusion-Based Image Super-Resolution in a Single Step [119.18813219518042]
  Super-resolution (SR) methods based on diffusion models exhibit promising results. But their practical application is hindered by the substantial number of required inference steps. We propose a simple yet effective method for achieving single-step SR generation, named SinSR.
  arXiv  Detail & Related papers  (2023-11-23T16:21:29Z)
• Single and Few-step Diffusion for Generative Speech Enhancement [18.487296462927034]
  Diffusion models have shown promising results in speech enhancement. In this paper, we address these limitations through a two-stage training approach. We show that our proposed method keeps a steady performance and therefore largely outperforms the diffusion baseline in this setting.
  arXiv  Detail & Related papers  (2023-09-18T11:30:58Z)
• LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement [118.83316133601319]
  Current deep learning methods for low-light image enhancement (LLIE) typically rely on pixel-wise mapping learned from paired data. We propose a degradation-aware learning scheme for LLIE using diffusion models, which effectively integrates degradation and image priors into the diffusion process.
  arXiv  Detail & Related papers  (2023-07-27T07:22:51Z)
• 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)

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.