Single-Stage Signal Attenuation Diffusion Model for Low-Light Image Enhancement and Denoising

• URL: http://arxiv.org/abs/2604.05727v1
• Date: Tue, 07 Apr 2026 11:33:04 GMT
• Title: Single-Stage Signal Attenuation Diffusion Model for Low-Light Image Enhancement and Denoising
• Abstract summary: Diffusion models excel at image restoration via probabilistic modeling of forward noise addition and reverse denoising.<n>Mainstream diffusion based Low-Light Image Enhancement (LLIE) methods either adopt a two-stage pipeline or an auxiliary correction network to refine U-Net outputs.<n>We propose the Signal Attenuation Diffusion Model (SADM), a novel diffusion process that integrates the signal attenuation mechanism into the diffusion pipeline.
• Score: 4.397450561168298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models excel at image restoration via probabilistic modeling of forward noise addition and reverse denoising, and their ability to handle complex noise while preserving fine details makes them well-suited for Low-Light Image Enhancement (LLIE). Mainstream diffusion based LLIE methods either adopt a two-stage pipeline or an auxiliary correction network to refine U-Net outputs, which severs the intrinsic link between enhancement and denoising and leads to suboptimal performance owing to inconsistent optimization objectives. To address these issues, we propose the Signal Attenuation Diffusion Model (SADM), a novel diffusion process that integrates the signal attenuation mechanism into the diffusion pipeline, enabling simultaneous brightness adjustment and noise suppression in a single stage. Specifically, the signal attenuation coefficient simulates the inherent signal attenuation of low-light degradation in the forward noise addition process, encoding the physical priors of low-light degradation to explicitly guide reverse denoising toward the concurrent optimization of brightness recovery and noise suppression, thereby eliminating the need for extra correction modules or staged training relied on by existing methods. We validate that our design maintains consistency with Denoising Diffusion Implicit Models(DDIM) via multi-scale pyramid sampling, balancing interpretability, restoration quality, and computational efficiency.

Related papers

• Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission [13.755711145996592]
  Lossless pixel-level image transmission is a fundamental regime beyond semantic communications.<n>This paper proposes a discrete symbol-diffusion-model-based separate source-channel coding framework for image transmission.
  arXiv  Detail & Related papers  (2026-06-04T15:14:31Z)
• ZeroIDIR: Zero-Reference Illumination Degradation Image Restoration with Perturbed Consistency Diffusion Models [72.4683657127516]
  We propose a zero-reference diffusion-based framework, named ZeroIDIR, for illumination image restoration.<n>The framework decouples the restoration process into adaptive illumination correction and diffusion-based reconstruction.<n>Experiments on publicly available benchmarks show that the proposed method outperforms state-of-the-art unsupervised competitors.
  arXiv  Detail & Related papers  (2026-05-12T02:36:49Z)
• Fewer Denoising Steps or Cheaper Per-Step Inference: Towards Compute-Optimal Diffusion Model Deployment [14.097906894386066]
  PostDiff is a training-free framework for accelerating pre-trained diffusion models.<n>We show that PostDiff can significantly improve the fidelity-efficiency trade-off of state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2025-08-08T09:29:37Z)
• A Simple Combination of Diffusion Models for Better Quality Trade-Offs in Image Denoising [43.44633086975204]
  We propose an intuitive method for leveraging pretrained diffusion models.<n>We then introduce our proposed Linear Combination Diffusion Denoiser.<n> LCDD achieves state-of-the-art performance and offers controlled, well-behaved trade-offs.
  arXiv  Detail & Related papers  (2025-03-18T19:02:19Z)
• Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising [22.897202020483576]
  Low-light photography produces images with low signal-to-noise ratios due to limited photons.<n>Deep-learning methods perform well, but they require large datasets of paired images that are impractical to acquire.<n>In this paper, we investigate the ability of diffusion models to capture the complex distribution of low-light noise.
  arXiv  Detail & Related papers  (2025-03-14T10:16:54Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors [38.96909959677438]
  Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. We devise a novel unsupervised LIE framework based on diffusion priors and lookup tables to achieve efficient low-light image recovery.
  arXiv  Detail & Related papers  (2024-09-27T16:37:27Z)
• Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising [16.43285056788183]
  We propose a novel approach called the Reconstruct-and-Generate Diffusion Model (RnG) Our method leverages a reconstructive denoising network to recover the majority of the underlying clean signal. It employs a diffusion algorithm to generate residual high-frequency details, thereby enhancing visual quality.
  arXiv  Detail & Related papers  (2023-09-19T16:01:20Z)
• Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
  We present a novel strategy called the Diffusion Model for Image Denoising (DMID) Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
  arXiv  Detail & Related papers  (2023-07-08T14:59:41Z)
• 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)
• Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising [137.5080784570804]
  Video denoising aims at removing noise from videos to recover clean ones. Some existing works show that optical flow can help the denoising by exploiting the additional spatial-temporal clues from nearby frames. We propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels.
  arXiv  Detail & Related papers  (2022-08-25T00:09:18Z)
• Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement [109.335317310485]
  Cycle-Interactive Generative Adversarial Network (CIGAN) is capable of not only better transferring illumination distributions between low/normal-light images but also manipulating detailed signals. In particular, the proposed low-light guided transformation feed-forwards the features of low-light images from the generator of enhancement GAN into the generator of degradation GAN.
  arXiv  Detail & Related papers  (2022-07-03T06:37:46Z)

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.