Fisheye image rectification using spherical and digital distortion models

Li, Xin; Pi, Yifei; Jia, Yanling; Yang, Yuhui; Chen, Zhiyong; Hou, Wenguang

doi:10.1117/12.2305834

Cited by 3 publications

(3 citation statements)

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“…Ying et al [44] presented a method to calibrate fish-eye lenses. Li et al [45] proposed a new fish-eye image rectification method that combines the physical spherical model and the digital distortion model. Krams et al [46] addressed the problem of people detection in top-view fish-eye imaging.…”

Section: Related Workmentioning

confidence: 99%

“…As discussed earlier, for many kinds of image degradations, many researches have been conducted to remove/reduce the degradation to restore the underlying clear images [15], [16], [17], [18], [19], [20], [21], [22], [25], [37], [38], [40], [42], [44], [45]. One interesting problem is whether we can get better classification accuracy by training the CNN classifiers on the clear images, and testing on the restored test images after the degradation removal.…”

Section: F Does Degradation-removal Pre-processing Help?mentioning

confidence: 99%

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Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Pei

Huang

Zou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

193

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Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional Neural Networks (CNN). Most of the existing works are focused on classifying very clear natural images, evidenced by the widely used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification. More specifically, we wonder whether image-classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image-classification performance. In this paper, we empirically study this problem for four kinds of degraded images -hazy images, underwater images, motion-blurred images and fish-eye images. For this study, we synthesize a large number of such degraded images by applying respective physical models to the clear natural images and collect a new hazy image dataset from the Internet. We expect this work can draw more interests from the community to study the classification of degraded images.

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Section: Related Workmentioning

confidence: 99%

Section: F Does Degradation-removal Pre-processing Help?mentioning

confidence: 99%

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Pei

Huang

Zou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

193

View full text Add to dashboard Cite

show abstract

“…However, owing to its extraordinary optical structure [8,9], the fisheye camera does not conform to the pinhole imaging principle and suffers significant distortion on the imaging plane [10,11], which could have a considerable impact on the high-precision measurement. Aiming at resolving this issue, Zheng et al [12] added high-order distortion to the traditional pinhole imaging model to represent the wide-angle lens imaging; however, this method did not correct the distortion of the fisheye image well, and the approach has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Calibration of underwater fisheye camera with multi-frequency heterodyne technique based on light field

Xie

Zhang

et al. 2020

Meas. Sci. Technol.

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In this paper, a novel approach is proposed to calibrate an underwater fisheye camera with the multi-frequency heterodyne technique based on the light field. First, an underwater fisheye camera imaging model based on the light field is established to demonstrate the multiple refractions of light in underwater propagation and the extraordinary imaging characteristics of the fisheye lens, which shows better conciseness and organization. Then, the calibration method applied in the underwater fisheye camera imaging system is proposed to determine the mapping of the light field and the pose of the underwater fisheye camera. The light field mapping of the fisheye lens is obtained with the assistance of the multi-frequency heterodyne technique. Finally, an underwater fisheye camera is developed and the experimental results are obtained to validate the correctness and feasibility of the proposed method. Our underwater imaging system proves capable of achieving higher accuracy in the context of underwater 3D measurement.

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Attentive Deep Stitching and Quality Assessment for 360$^{\circ }$ Omnidirectional Images

Zhao

et al. 2020

IEEE J. Sel. Top. Signal Process.

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Fisheye image rectification using spherical and digital distortion models

Cited by 3 publications

References 0 publications

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Calibration of underwater fisheye camera with multi-frequency heterodyne technique based on light field

Attentive Deep Stitching and Quality Assessment for 360$^{\circ }$ Omnidirectional Images

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