Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing (1805.05308v1)

Abstract: In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner. Moreover, the proposed approach does not rely on estimation of the atmospheric scattering model parameters. Our method enhances CycleGAN formulation by combining cycle-consistency and perceptual losses in order to improve the quality of textural information recovery and generate visually better haze-free images. Typically, deep learning models for dehazing take low resolution images as input and produce low resolution outputs. However, in the NTIRE 2018 challenge on single image dehazing, high resolution images were provided. Therefore, we apply bicubic downscaling. After obtaining low-resolution outputs from the network, we utilize the Laplacian pyramid to upscale the output images to the original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE datasets. Extensive experiments demonstrate that the proposed approach improves CycleGAN method both quantitatively and qualitatively.

• The paper introduces Cycle-Dehaze, an enhanced CycleGAN approach for effective single image dehazing.
• It implements cycle consistency and innovative training techniques to significantly improve image restoration.
• Experimental results demonstrate superior performance in metrics like PSNR and SSIM over existing dehazing methods.

Analyzing the CVPR Author Guidelines Paper

The document titled "LaTeX Author Guidelines for CVPR Proceedings" serves a fundamental purpose within the academic community by delineating the formatting requirements for manuscript submissions to the Computer Vision and Pattern Recognition (CVPR) conference. While it ostensibly appears to be a standard style guide, a closer inspection reveals that it embodies the intricacies of academic communications, offering valuable insights for prospective authors.

Key Aspects of the Paper

Manuscript Preparation

The paper meticulously outlines the requirements for manuscript preparation, highlighting critical formatting elements like paper length, type-style, margins, and page numbering. Notably, it enforces an eight-page limit, exclusive of references, and demands compliance through strict formatting guidelines. Enhancing the structure of submissions, it specifies the use of \LaTeX\ commands to maintain uniformity in paper dimensions and typographic style. The insistence on precise adherence ensures consistency across all submissions, thereby facilitating a streamlined review process.

Blind Review Process

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Mathematical and Graphical Presentation

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Implications and Future Prospects

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Looking ahead, the guidelines might evolve to incorporate adaptive document preparation systems, leveraging artificial intelligence to automatically format submissions according to predefined standards. Such developments could further reduce barriers in academic publishing, promoting inclusivity and diversifying the range of contributors.

Conclusion

In summary, the paper offers more than a mere directive on manuscript preparation; it elucidates a refined understanding of academic protocol within the CVPR community. Through its rigorous stipulations, it acts as a beacon guiding authors toward effective communication of their scientific endeavor. As the field continues to advance, these guidelines lay foundational support for the forthcoming innovations in academic publishing and peer review methodologies.