Over the years, many methods have emerged to solve the super-resolution problem of light field images, and among them, those methods based on deep learning are noted quite attractive recently. Although the features extracted from epipolar domain for the super-resolution of light field images are actively investigated due to their potential capability of well capturing the relationship between spatial and angular domains, we note that spatial features are still the most important foundation in feature extraction. In this paper, we design a network, named as LFSelectSR, employing multiple convolutional kernels to fully extract spatial features and introduce a dynamic selection mechanism that can extract the most valuable spatial features. By training and testing the network using well-known datasets, we demonstrate its excellent performance of achieving the level of state-of-the-arts under certain conditions.
In the perspective of alleviating the inherent trade-off between the spatial and angular resolutions of light field (LF) images, much research has been carried out to increase the angular resolution of LFs by synthesizing intermediate views. Since the height of each EPI is equal to the angular resolution of LF, we tackle the view synthesis problem as doubling the height of each EPI in LF. To efficiently stretch the EPI while not consuming too much computing time, we propose to first segment the EPI into superpixels and then adaptively interpolate each superpixel separately. The test results on the synthetic and real-scene LF datasets show that our scheme can achieve average Peak signal-to-noise ratio (PSNR) / structural similarity index measure (SSIM) around 30.58dB / 0.9131 and 32.28dB / 0.9510, by taking computing time of 5.80 minute and 1.83 minute for HCI and EPFL dataset, respectively.
In order to alleviate the inherent trade-off relation between spatial and angular resolutions of light field (LF) images, many experiments have been carried out to enhance the angular resolution of LFs by creating novel views. In this paper, we investigate a method to enhance the angular resolution of LF image by first grouping the pixels within and across the multiple views into LF superpixels using existing LF segmentation method, then generating novel views by shifting and overlaying the layers containing the LF superpixels having similar per-view disparity values. Experimental results with synthetic and real-scene datasets show that our method achieves good quality of reconstruction.
Light field (LF) image is captured by plenoptic cameras which suffers from trade-off between spatial and angular resolutions. Numerous methods have been proposed to enhance the spatial resolution of images captured by LF cameras. Among the state-of-the-art methods, there is an approach to super resolve LF images using the graph-based regularization. However, it has a problem of taking too much time for execution. In this paper, we propose a method to simplify the process in computing graph. The experimental results show that our proposed method can reduce up to 18% of time complexity compared to the original approach while maintaining the image quality of LF images.
High-quality depth estimation from light field (LF) image is an important and challenging task for which many algorithms have been developed so far. While compression is inevitably required in practice for LF data due to its huge data amount, most depth estimation methods have not yet paid sufficient attention to the effect of compression on it. In this paper, we investigate various LF depth estimation methods to design a LF compression method in the context of good depth estimation. By noting that building the data cost is a very first step in most depth estimation algorithms and the data cost computation has a great impact on eventual quality of the depth image, in this paper, we present an in-depth analysis of data cost computation in LF depth estimation problem in the context of compression. Our results show that the data cost building on Epipolar Plane Image (EPI) outperforms other tested methods in this paper and is more robust to compression.
Light Field (LF) image/video data provides both spatial and angular information of scene but at the cost of tremendous data volume for their storage and transmission. At the moment, the MPEG Multi-view Video Coding (MVC) is one of promising compression solutions for LF video data, so it deserves much investigation for better prediction structure to effectively reduce the redundancy in LF video data. Several prediction structures have been investigated but only with limited experimental evaluations due to lack of dataset and non-identical testing configurations. This practical problem can be mitigated now by availability of new datasets and common test condition recently proposed by MPEG. As the first step for designing a good compression method for LF video data, in this paper, we evaluate the performance of existing prediction structures for MVC-based LF video coding methods following the MPEG common test condition and its dataset.
In video-based light field coding, sub-aperture images (SAIs) are ordered to form a pseudo video sequence, and the sequence is encoded by a video compression algorithm, for example, by HEVC. When the size of SAI is not divisible by the minimum size of coding tree unit, proper boundary handling method is required. This paper investigates several boundary handling methods. To maintain high quality of the central SAIs, we combine rotation and u scans to have a new hybrid scan order. Random access configuration is used instead of low-delay one for better coding efficiency. The proposed methods are evaluated with the latest coding tool.
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