Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Open accessAbstract
Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise (1*1) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10~1/100 network parameters and computational cost while achieving comparable performance.
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Recently, computationally efficient networks like MobileNet have been proposed by leveraging depth-wise convolutions, point-wise convolutions, or binary operations.
“Recently, some computationally efficient networks like MobileNet have been proposed by leveraging depth-wise convolutions [65], point-wise convolutions [66], or binary operations [67].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [66]
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- Authors
- Zhang, J., Cao, Y., Wang, Y., Wen, C., & Chen, C. W.
- Affiliations
- Hangzhou Dianzi University (CN)
- University of Science and Technology of China (CN)
- University at Buffalo, State University of New York (US)
- Venue
- Proceedings of the 26th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2018-10-15
- Pages
- 984-992
- OpenAlex
- W2962816068
- Cited by
- 40
- References
- 44
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 6 · 2022: 4 · 2021: 9 · 2020: 8 · 2019: 8 · 2018: 1
- FWCI
- 1.93
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61751304, 61806062, 61873077, 61472380)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Color Science and Applications
- Keywords
- 1x1 convolution, pixel shuffling, natural image statistics, color constancy, image dehazing, image priors
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