Robust Reflection Removal Based on Light Field Imaging

Li, Tingtian; Lun, Daniel Pak-Kong; Chan, Yuk‐Hee; Budianto,

doi:10.1109/tip.2018.2880510

Cited by 33 publications

(9 citation statements)

References 31 publications

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“…Recently, light field imaging technique draw a wide attention. In image processing task, Lu et al used light field images with CNN for depth map restoration to cope with turbidity of water [8], and Li et al used them for reflection removal [9]. Jeon et al proposed an accurate depth map estimation method using light field camera [10].…”

Section: Related Workmentioning

confidence: 99%

Underwater Stereo Using Refraction-Free Image Synthesized From Light Field Camera

Ichimaru

Kawasaki

2019

2019 IEEE International Conference on Image Processing (ICIP)

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There is a strong demand on capturing underwater scenes without distortions caused by refraction. Since a light field camera can capture several light rays at each point of an image plane from various directions, if geometrically correct rays are chosen, it is possible to synthesize a refraction-free image. In this paper, we propose a novel technique to efficiently select such rays to synthesize a refraction-free image from an underwater image captured by a light field camera. In addition, we propose a stereo technique to reconstruct 3D shapes using a pair of our refraction-free images, which are central projection. In the experiment, we captured several underwater scenes by two light field cameras, synthesized refraction free images and applied stereo technique to reconstruct 3D shapes. The results are compared with previous techniques which are based on approximation, showing the strength of our method.

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Section: Related Workmentioning

confidence: 99%

Underwater Stereo Using Refraction-Free Image Synthesized From Light Field Camera

Ichimaru

Kawasaki

2019

2019 IEEE International Conference on Image Processing (ICIP)

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“…A LTHOUGH light field (LF) cameras enable many attractive functions such as post-capture image editing [1]- [3], depth sensing [4]- [9], saliency detection [10]- [14], and de-occlusion [15]- [17], the resolution of a sub-aperture image (SAI) is much lower than that of the total sensors. The low spatial resolution problem hinders the development of LF imaging [18].…”

Section: Introductionmentioning

confidence: 99%

Light Field Image Super-Resolution Using Deformable Convolution

Wang

Yang

Wang

et al. 2021

IEEE Trans. on Image Process.

122

116

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“…From recorded spatial and angular data, multi-view images of a scene can be reconstructed. These light field images have been used in many computer vision tasks, such as saliency detection [2], [3], depth sensing [4], [5], de-occlusion [6]- [8]. However, light field images have low spatial resolution due to the trade-off between angular and spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

Light Field Image Super-Resolution Based on Multilevel Structures

Park¹,

Shin

Kim

et al. 2022

IEEE Access

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Light field (LF) images suffer from low spatial resolution due to the trade-off between angular and spatial resolutions. Thus, spatial super-resolution (SR) of LF images is an essential task to obtain high-quality LF images. However, the existing SR networks still have limitations, since they exploit only single-level features to use sub-pixel information in LF images. In this paper, we proposed a light field super-resolution (LFSR) network to effectively improve the spatial resolution of light field images. The proposed network takes one target image and its 8-neighboring images for references. We construct multilevel structures for the proposed network to effectively estimate and mix sub-pixel information in reference images. The proposed network is composed of a feature extractor, a feature warping module, a feature mixing module, and a upscaling module. The feature extractor provides multi-level features for SR and offsets to the feature warping module to obtain aligned features for multiple reference images. The feature mixing module mixes multiple aligned features based on the similarity between the target and reference images to obtain multi-level mixed features. Finally, the upscaling module generates a high-resolution residual image using the multi-level mixed features. Experimental results demonstrate the proposed network outperforms the state-of-the-art methods on various light field datasets. The pre-trained model and source codes are available at https://github.com/Hwa-Jong/LF_MLS.

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Robust Reflection Removal Based on Light Field Imaging

Cited by 33 publications

References 31 publications

Underwater Stereo Using Refraction-Free Image Synthesized From Light Field Camera

Underwater Stereo Using Refraction-Free Image Synthesized From Light Field Camera

Light Field Image Super-Resolution Using Deformable Convolution

Light Field Image Super-Resolution Based on Multilevel Structures

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