Combining Pre- and Post-Demosaicking Noise Removal for RAW Video

• URL: http://arxiv.org/abs/2410.02572v1
• Date: Thu, 3 Oct 2024 15:20:19 GMT
• Title: Combining Pre- and Post-Demosaicking Noise Removal for RAW Video
• Authors: Marco Sánchez-Beeckman, Antoni Buades, Nicola Brandonisio, Bilel Kanoun,
• Abstract summary: Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. We propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data. We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking.
• Score: 2.772895608190934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. It is generally performed early in the pipeline, usually before demosaicking, although studies swapping their order or even conducting them jointly have been proposed. With the advent of deep learning, the quality of denoising algorithms has steadily increased. Even so, modern neural networks still have a hard time adapting to new noise levels and scenes, which is indispensable for real-world applications. With those in mind, we propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data. We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking. We also integrate temporal trajectory prefiltering steps before each denoiser, which further improve texture reconstruction. The proposed method only requires an estimation of the noise model at the sensor, accurately adapts to any noise level, and is competitive with the state of the art, making it suitable for real-world videography.

Related papers

• Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy [44.09909260046396]
  We propose AdaptiveDiffusion to reduce noise prediction steps during the denoising process. Our method can significantly speed up the denoising process while generating identical results to the original process, achieving up to an average 25x speedup.
  arXiv  Detail & Related papers  (2024-10-13T15:19:18Z)
• How to Best Combine Demosaicing and Denoising? [16.921538543268216]
  demosaicing and denoising play a critical role in the raw imaging pipeline. Most demosaicing methods address the demosaicing of noise free images. The real problem is to jointly denoise and demosaic noisy raw images.
  arXiv  Detail & Related papers  (2024-08-13T07:23:53Z)
• Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer [17.430622649002427]
  Existing learning-based denoising methods typically train models to generalize the image prior from large-scale datasets. We propose a new perspective on the denoising challenge by highlighting the distinct separation between noise and image priors. We introduce a Locally Noise Prior Estimation algorithm, which accurately estimates the noise prior directly from a single raw noisy image.
  arXiv  Detail & Related papers  (2024-07-12T08:43:11Z)
• Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model [83.9497193551511]
  We introduce Lighting Every Darkness (LED), which is effective regardless of the digital gain or the camera sensor. LED eliminates the need for explicit noise model calibration, instead utilizing an implicit fine-tuning process that allows quick deployment and requires minimal data. LED also allows researchers to focus more on deep learning advancements while still utilizing sensor engineering benefits.
  arXiv  Detail & Related papers  (2023-08-07T10:09:11Z)
• Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images [35.29066692454865]
  This paper proposes a framework for training a noise model and a denoiser simultaneously. It relies on pairs of noisy images rather than noisy/clean paired image data. The trained denoiser is shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches.
  arXiv  Detail & Related papers  (2022-06-02T15:31:40Z)
• 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)
• IDR: Self-Supervised Image Denoising via Iterative Data Refinement [66.5510583957863]
  We present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
  arXiv  Detail & Related papers  (2021-11-29T07:22:53Z)
• Rethinking Noise Synthesis and Modeling in Raw Denoising [75.55136662685341]
  We introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors.
  arXiv  Detail & Related papers  (2021-10-10T10:45:24Z)
• Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation [52.75909685172843]
  Real-world image noise removal is a long-standing yet very challenging task in computer vision. We propose a novel unified framework to deal with the noise removal and noise generation tasks. Our method learns the joint distribution of the clean-noisy image pairs.
  arXiv  Detail & Related papers  (2020-07-12T09:16:06Z)
• Learning Model-Blind Temporal Denoisers without Ground Truths [46.778450578529814]
  Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises. Previous image-based method leads to noise overfitting if directly applied to video denoisers. We propose a general framework for video denoising networks that successfully addresses these challenges.
  arXiv  Detail & Related papers  (2020-07-07T07:19:48Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.