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Noisier2Noise: Learning to Denoise from Unpaired Noisy Data (1910.11908v1)

Published 25 Oct 2019 in eess.IV and cs.CV

Abstract: We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise.

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Authors (4)
  1. Nick Moran (7 papers)
  2. Dan Schmidt (6 papers)
  3. Yu Zhong (27 papers)
  4. Patrick Coady (1 paper)
Citations (205)

Summary

Overview of "LaTeX Author Guidelines for CVPR Proceedings"

The paper presented provides comprehensive guidelines for authors preparing submissions for the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) proceedings using LaTeX. The goal is to ensure uniformity in the format and presentation of papers, facilitating a smoother peer review process and maintaining the professional standard of the conference publications. This document addresses multiple facets of the submission process, including format specifications, submission protocols, and necessary compliance with IEEE policies.

Key Sections and Guidelines

  1. Manuscript Language: The guidelines stipulate that all submissions should be in English, promoting a standardized medium for international scholarly communication.
  2. Dual Submission Policy: Authors are required to adhere to the policy on dual submissions, ensuring that works are not under review for multiple venues simultaneously. This policy is reiterated to maintain the integrity of the academic review process.
  3. Paper Length and Formatting: Papers must not exceed eight pages in length, excluding references. The document explicitly states that overlength papers will not be reviewed, emphasizing the strict adherence to page limits. Formatting guidance includes specifics of typeface, spacing, and justification, ensuring a consistent visual presentation across all submitted works.
  4. Blind Review Process: The paper provides detailed instructions for maintaining anonymity in submissions to uphold the principles of blind review. Authors must avoid self-referential language and ensure that their previous work is referred to in third-person. The guidelines offer practical examples of acceptable and unacceptable phrasing.
  5. Mathematical Notation and Equations: Authors are instructed to number all sections and displayed equations to facilitate easier reference. This assists in maintaining clarity and precision in complex mathematical expositions.
  6. Illustrations and Graphics: The guidelines emphasize the importance of clear and high-resolution graphics, which should be comprehensible in a printed format. This underscores the necessity for visual elements to complement rather than detract from the textual content.
  7. Final Copy and Copyright: Authors must submit a signed copyright release form for their submissions to be included in the proceedings, highlighting compliance with intellectual property policies.

Implications and Future Considerations

Adhering to these detailed guidelines is crucial for researchers aiming to contribute to CVPR, reflecting the conference's commitment to maintaining high publication standards. Consistent formatting allows for efficient dissemination and evaluation of research findings, fostering an environment of rigorous academic scholarship.

For future iterations of conferences such as CVPR, these guidelines could evolve to incorporate advancements in digital publishing and accessibility standards, reflecting the ongoing transformation of academic communication. Additionally, exploring automation in format compliance checking could streamline submissions, allowing authors to focus more on content quality rather than formatting intricacies.

Ultimately, these guidelines provide a clear framework within which researchers can present their work, ensuring that the scientific contributions are evaluated on their merit, unimpeded by variations in presentation style. This structured approach supports the professional integrity and the high esteem in which CVPR proceedings are held across the computer vision and broader machine learning research communities.