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Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 12364

Special Issue Editor

Prof. Dr. Kuo-Liang Chung

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10672, Taiwan
Interests: image processing, image coding, and deep learning for computer vision

Special Issue Information

Dear Colleagues,

In this Special Issue, we hope to invite you to contribute your new research results about compression and image processing for CFA images, light field images, and point cloud. With the acquired three kinds of sensing data, topics of interest in this Special Issue include (but are not limited to) noise removal, demosaicking, compression, super resolution, depth estimation, matching, 3D reconstruction, registration, segmentation, and deep learning applications.

Prof. Dr. Kuo-Liang Chung
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • noise removal algorithm
  • demosaicking CFA images
  • compression
  • image processing for light field images
  • registration and segmentation for point cloud
  • deep learning applications

Published Papers (6 papers)

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Research

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19 pages, 20479 KiB  
Article
FPattNet: A Multi-Scale Feature Fusion Network with Occlusion Awareness for Depth Estimation of Light Field Images
by Min Xiao, Chen Lv and Xiaomin Liu
Sensors 2023, 23(17), 7480; https://doi.org/10.3390/s23177480 - 28 Aug 2023
Cited by 1 | Viewed by 946
Abstract
A light field camera can capture light information from various directions within a scene, allowing for the reconstruction of the scene. The light field image inherently contains the depth information of the scene, and depth estimations of light field images have become a [...] Read more.
A light field camera can capture light information from various directions within a scene, allowing for the reconstruction of the scene. The light field image inherently contains the depth information of the scene, and depth estimations of light field images have become a popular research topic. This paper proposes a depth estimation network of light field images with occlusion awareness. Since light field images contain many views from different viewpoints, identifying the combinations that contribute the most to the depth estimation of the center view is critical to improving the depth estimation accuracy. Current methods typically rely on a fixed set of views, such as vertical, horizontal, and diagonal, which may not be optimal for all scenes. To address this limitation, we propose a novel approach that considers all available views during depth estimation while leveraging an attention mechanism to assign weights to each view dynamically. By inputting all views into the network and employing the attention mechanism, we enable the model to adaptively determine the most informative views for each scene, thus achieving more accurate depth estimation. Furthermore, we introduce a multi-scale feature fusion strategy that amalgamates contextual information and expands the receptive field to enhance the network’s performance in handling challenging scenarios, such as textureless and occluded regions. Full article
(This article belongs to the Special Issue Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud)
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28 pages, 22448 KiB  
Article
Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses
by Emil Dumic and Luis A. da Silva Cruz
Sensors 2023, 23(12), 5623; https://doi.org/10.3390/s23125623 - 15 Jun 2023
Cited by 1 | Viewed by 2222
Abstract
This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC [...] Read more.
This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds. The study involved compressing a set of test dynamic point clouds using the MPEG V-PCC (Video-based Point Cloud Compression) codec at 5 different levels of compression and applying simulated packet losses with three packet loss rates (0.5%, 1% and 2%) to the V-PCC sub-bitstreams prior to decoding and reconstructing the dynamic point clouds. The recovered dynamic point clouds qualities were then assessed by human observers in experiments conducted at two research laboratories in Croatia and Portugal, to collect MOS (Mean Opinion Score) values. These scores were subject to a set of statistical analyses to measure the degree of correlation of the data from the two laboratories, as well as the degree of correlation between the MOS values and a selection of objective quality measures, while taking into account compression level and packet loss rates. The subjective quality measures considered, all of the full-reference type, included point cloud specific measures, as well as others adapted from image and video quality measures. In the case of image-based quality measures, FSIM (Feature Similarity index), MSE (Mean Squared Error), and SSIM (Structural Similarity index) yielded the highest correlation with subjective scores in both laboratories, while PCQM (Point Cloud Quality Metric) showed the highest correlation among all point cloud-specific objective measures. The study showed that even 0.5% packet loss rates reduce the decoded point clouds subjective quality by more than 1 to 1.5 MOS scale units, pointing out the need to adequately protect the bitstreams against losses. The results also showed that the degradations in V-PCC occupancy and geometry sub-bitstreams have significantly higher (negative) impact on decoded point cloud subjective quality than degradations of the attribute sub-bitstream. Full article
(This article belongs to the Special Issue Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud)
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12 pages, 2432 KiB  
Article
Light Field Image Super-Resolution Using Deep Residual Networks on Lenslet Images
by Ahmed Salem, Hatem Ibrahem and Hyun-Soo Kang
Sensors 2023, 23(4), 2018; https://doi.org/10.3390/s23042018 - 10 Feb 2023
Viewed by 1824
Abstract
Due to its widespread usage in many applications, numerous deep learning algorithms have been proposed to overcome Light Field’s trade-off (LF). The sensor’s low resolution limits angular and spatial resolution, which causes this trade-off. The proposed method should be able to model the [...] Read more.
Due to its widespread usage in many applications, numerous deep learning algorithms have been proposed to overcome Light Field’s trade-off (LF). The sensor’s low resolution limits angular and spatial resolution, which causes this trade-off. The proposed method should be able to model the non-local properties of the 4D LF data fully to mitigate this problem. Therefore, this paper proposes a different approach to increase spatial and angular information interaction for LF image super-resolution (SR). We achieved this by processing the LF Sub-Aperture Images (SAI) independently to extract the spatial information and the LF Macro-Pixel Image (MPI) to extract the angular information. The MPI or Lenslet LF image is characterized by its ability to integrate more complementary information between different viewpoints (SAIs). In particular, we extract initial features and then process MAI and SAIs alternately to incorporate angular and spatial information. Finally, the interacted features are added to the initial extracted features to reconstruct the final output. We trained the proposed network to minimize the sum of absolute errors between low-resolution (LR) input and high-resolution (HR) output images. Experimental results prove the high performance of our proposed method over the state-of-the-art methods on LFSR for small baseline LF images. Full article
(This article belongs to the Special Issue Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud)
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23 pages, 4599 KiB  
Article
IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning
by Ijaz Ahmad and Seokjoo Shin
Sensors 2022, 22(20), 8074; https://doi.org/10.3390/s22208074 - 21 Oct 2022
Cited by 3 | Viewed by 1788
Abstract
Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block–based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input [...] Read more.
Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block–based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input color image as a pseudo grayscale image to benefit from a smaller block size. However, such representation degrades image quality and compression savings, and removes color information, which limits their applications. To solve these limitations, we proposed inter and intra block processing for compressible PE methods (IIB–CPE). The method represents an input as a color image and performs block-level inter processing and sub-block-level intra processing on it. The intra block processing results in an inside–out geometric transformation that disrupts the symmetry of an entire block thus achieves visual encryption of local details while preserving the global contents of an image. The intra block-level processing allows the use of a smaller block size, which improves encryption efficiency without compromising compression performance. Our analyses showed that IIB–CPE offers 15% bitrate savings with better image quality than the existing PE methods. In addition, we extended the scope of applications of the proposed IIB–CPE to the privacy-preserving deep learning (PPDL) domain. Full article
(This article belongs to the Special Issue Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud)
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13 pages, 2806 KiB  
Article
SUTO-Solar Through-Turbulence Open Image Dataset
by Adam Popowicz and Valeri Orlov
Sensors 2022, 22(20), 7902; https://doi.org/10.3390/s22207902 - 17 Oct 2022
Viewed by 1714
Abstract
Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially [...] Read more.
Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially dependent blurring induced by the phase modulation of the light wavefront is removed. The number of turbulence-disturbed image databases available online is small, and the datasets usually contain repeating types of ground objects (cars, buildings, ships, chessboard patterns). In this article, we present a database of solar images in widely varying turbulence conditions obtained from the SUTO-Solar patrol station recorded over a period of more than a year. The dataset contains image sequences of distinctive yet randomly selected fragments of the solar chromosphere and photosphere. Reference images have been provided with the data using computationally intensive image recovery with the latest multiframe blind deconvolution technique, which is widely accepted in solar imaging. The presented dataset will be extended in the next few years as new image sequences are routinely acquired each sunny day at the SUTO-Solar station. Full article
(This article belongs to the Special Issue Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud)
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Review

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25 pages, 15128 KiB  
Review
Compression for Bayer CFA Images: Review and Performance Comparison
by Kuo-Liang Chung, Hsuan-Ying Chen, Tsung-Lun Hsieh and Yen-Bo Chen
Sensors 2022, 22(21), 8362; https://doi.org/10.3390/s22218362 - 31 Oct 2022
Cited by 1 | Viewed by 2524
Abstract
Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA [...] Read more.
Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA images. These compression methods can be roughly divided into the compression-first-based (CF-based) scheme and the demosaicing-first-based (DF-based) scheme. However, in the literature, no review article for the two compression schemes and their compression performance is reported. In this article, the related CF-based and DF-based compression works are reviewed first. Then, the testing Bayer CFA images created from the Kodak, IMAX, screen content images, videos, and classical image datasets are compressed on the Joint Photographic Experts Group-2000 (JPEG-2000) and the newly released Versatile Video Coding (VVC) platform VTM-16.2. In terms of the commonly used objective quality, perceptual quality metrics, the perceptual effect, and the quality–bitrate tradeoff metric, the compression performance comparison of the CF-based compression methods, in particular the reversible color transform-based compression methods and the DF-based compression methods, is reported and discussed. Full article
(This article belongs to the Special Issue Advances in Coding, Sensing, and Processing for CFA Images, Light Field Images, and Point Cloud)
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