Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion

• URL: http://arxiv.org/abs/2608.29227v1
• Date: Sat, 29 Aug 2026 12:33:24 GMT
• Title: Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
• Abstract summary: We formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding.<n>The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation.
• Score: 15.907930410747232
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation which steer the diffusion process. We apply this novel energy formulation to image reconstruction problems, showing good performance in the presence of mixtures of Gaussian and impulse-like noise, e.g. missing data. In denoising experiments of common scalar-valued images our approach performs competitive compared to other diffusion schemes as well as state-of-the-art denoising methods for the considered noise types.

Related papers

• A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression [21.919751666534395]
  Noise Constrained Diffusion (NC-Diffusion) for high fidelity image compression.<n>NC-Diffusion formulates the quantization noise originally added in the learned image compression as the noise in the forward process of diffusion.<n> adaptive frequency-domain filtering module is developed to enhance the skip connections in the U-Net based diffusion architecture.
  arXiv  Detail & Related papers  (2026-04-08T01:35:15Z)
• Reinforced Diffusion: Learning to Push the Limits of Anisotropic Diffusion for Image Denoising [57.226775716102765]
  We describe a trainable anisotropic diffusion framework based on reinforcement learning.<n>By modeling the denoising process as a series of naive diffusion actions with iterations order learned by deep Q-learning, we propose an effective diffusion-based image denoiser.
  arXiv  Detail & Related papers  (2025-12-30T07:23:15Z)
• VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference [5.852077003870417]
  We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.<n>We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.
  arXiv  Detail & Related papers  (2024-11-28T05:35:36Z)
• Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
  We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters. We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
  arXiv  Detail & Related papers  (2024-11-17T17:45:37Z)
• Ultrasound Image Enhancement with the Variance of Diffusion Models [7.360352432782388]
  Enhancing ultrasound images requires a delicate balance between contrast, resolution, and speckle preservation. This paper introduces a novel approach that integrates adaptive beamforming with denoising diffusion-based variance imaging.
  arXiv  Detail & Related papers  (2024-09-17T17:29:33Z)
• 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 Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• Denoising Diffusion Gamma Models [91.22679787578438]
  We introduce the Denoising Diffusion Gamma Model (DDGM) and show that noise from Gamma distribution provides improved results for image and speech generation. Our approach preserves the ability to efficiently sample state in the training diffusion process while using Gamma noise.
  arXiv  Detail & Related papers  (2021-10-10T10:46:31Z)
• Non Gaussian Denoising Diffusion Models [91.22679787578438]
  We show that noise from Gamma distribution provides improved results for image and speech generation. We also show that using a mixture of Gaussian noise variables in the diffusion process improves the performance over a diffusion process that is based on a single distribution.
  arXiv  Detail & Related papers  (2021-06-14T16:42:43Z)

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.