NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN

• URL: http://arxiv.org/abs/2312.10112v3
• Date: Thu, 31 Oct 2024 12:19:37 GMT
• Title: NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN
• Authors: Young Joo Han, Ha-Jin Yu,
• Abstract summary: NM-FlowGAN is a hybrid approach that exploits the strengths of both GAN and Normalizing Flows. Our method synthesizes noise using clean images and factors that affect noise characteristics, such as easily obtainable parameters like camera type and ISO settings. In our experiments, our NM-FlowGAN outperforms other baselines in the sRGB noise synthesis task.
• Score: 9.81778202920426
• License:
• Abstract: Modeling and synthesizing real sRGB noise is crucial for various low-level vision tasks, such as building datasets for training image denoising systems. The distribution of real sRGB noise is highly complex and affected by a multitude of factors, making its accurate modeling extremely challenging. Therefore, recent studies have proposed methods that employ data-driven generative models, such as Generative Adversarial Networks (GAN) and Normalizing Flows. These studies achieve more accurate modeling of sRGB noise compared to traditional noise modeling methods. However, there are performance limitations due to the inherent characteristics of each generative model. To address this issue, we propose NM-FlowGAN, a hybrid approach that exploits the strengths of both GAN and Normalizing Flows. We combine pixel-wise noise modeling networks based on Normalizing Flows and spatial correlation modeling networks based on GAN. Specifically, the pixel-wise noise modeling network leverages the high training stability of Normalizing Flows to capture noise characteristics that are affected by a multitude of factors, and the spatial correlation networks efficiently model pixel-to-pixel relationships. In particular, unlike recent methods that rely on paired noisy images, our method synthesizes noise using clean images and factors that affect noise characteristics, such as easily obtainable parameters like camera type and ISO settings, making it applicable to various fields where obtaining noisy-clean image pairs is not feasible. In our experiments, our NM-FlowGAN outperforms other baselines in the sRGB noise synthesis task. Moreover, the denoising neural network trained with synthesized image pairs from our model shows superior performance compared to other baselines. Our code is available at: \url{https://github.com/YoungJooHan/NM-FlowGAN}.

Related papers

• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
  High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. DNNs still generate ghosting artifacts when LDR images have saturation and large motion. We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
  arXiv  Detail & Related papers  (2023-11-02T01:53:55Z)
• Towards General Low-Light Raw Noise Synthesis and Modeling [37.87312467017369]
  We introduce a new perspective to synthesize the signal-independent noise by a generative model. Specifically, we synthesize the signal-dependent and signal-independent noise in a physics- and learning-based manner. In this way, our method can be considered as a general model, that is, it can simultaneously learn different noise characteristics for different ISO levels.
  arXiv  Detail & Related papers  (2023-07-31T09:10:10Z)
• Realistic Noise Synthesis with Diffusion Models [68.48859665320828]
  Deep image denoising models often rely on large amount of training data for the high quality performance. We propose a novel method that synthesizes realistic noise using diffusion models, namely Realistic Noise Synthesize Diffusor (RNSD) RNSD can incorporate guided multiscale content, such as more realistic noise with spatial correlations can be generated at multiple frequencies.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising [9.119226249676501]
  Hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues.
  arXiv  Detail & Related papers  (2023-05-06T13:28:20Z)
• Modeling sRGB Camera Noise with Normalizing Flows [35.29066692454865]
  We propose a new sRGB-domain noise model based on normalizing flows that is capable of learning the complex noise distribution found in sRGB images under various ISO levels. Our normalizing flows-based approach outperforms other models by a large margin in noise modeling and synthesis tasks.
  arXiv  Detail & Related papers  (2022-06-02T00:56:34Z)
• Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training [50.018580462619425]
  We propose a novel framework, namely Pixel-level Noise-aware Generative Adrial Network (PNGAN) PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space. For better noise fitting, we present an efficient architecture Simple Multi-versa-scale Network (SMNet) as the generator.
  arXiv  Detail & Related papers  (2022-04-06T14:09:02Z)
• Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis [148.16279746287452]
  We propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise. Experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-24T18:11:31Z)

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.