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Special Issue "Advance and Applications of RGB Sensors"

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

Deadline for manuscript submissions: closed (15 September 2019) | Viewed by 8975

Special Issue Editor

Dr. Masayuki TANAKA
E-Mail Website
Guest Editor
1. National Institute of Advanced Industrial Science and Technology (AIST),Tokyo, Japan
2. Tokyo Institute of Technology, Department of Systems and Control Engineering, Tokyo, Japan
Interests: Gradient-Based Image Processing; Image Processing for a Single Sensor; Multispectral Image Processing, Convolutoinal Nerual Network for Image Processing

Special Issue Information

Dear Colleagues,

A single-chip color image sensor, typically an RGB sensor, is an essential component of sensing system and computer vision applications. The key to using the single-chip sensor is an associated color imaging process pipeline such as color demosaicking, denoising, color correction, etc.

In this Special Issue, a wide range of topics are covered, including color demosaicking, denoising, color correction, color filter array design, multispectral color filter array, the design of the spectral sensitivity function, and high-dynamic range imaging.

Dr. Masayuki TANAKA
Guest Editor

Manuscript Submission Information

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Keywords

  • Color demosaicking
  • Color correction
  • Denoising
  • Multispectral filter array (MSFA)
  • Noise analysis method
  • Design of spectral sensitivity function
  • High-dynamic range imaging

Published Papers (6 papers)

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Research

Article
Modeling and Analysis of Spatial Inter-Symbol Interference for RGB Image Sensors Based on Visible Light Communication
Sensors 2019, 19(22), 4999; https://doi.org/10.3390/s19224999 - 16 Nov 2019
Cited by 1 | Viewed by 905
Abstract
In this paper, RGB image sensors based on visible light communication system are designed and researched. In our study, the spatial inter-symbol interference is considered and the formation mechanism of the stray light, the influence of the spatial inter-symbol interference, and the influence [...] Read more.
In this paper, RGB image sensors based on visible light communication system are designed and researched. In our study, the spatial inter-symbol interference is considered and the formation mechanism of the stray light, the influence of the spatial inter-symbol interference, and the influence of RGB image sensors are analyzed. The mathematics expression of the system signal-to-noise ratio (SNR) and bit error ratio (BER) is given. The simulation result indicates that there is a critical communication distance in the system. Once the communication distance exceeds the critical value, the system BER performance increases sharply. In addition, an adaptive threshold detection method is introduced and the performance is simulated. By means of estimating the spatial inter-symbol interference noise power, the optimal detection threshold can be obtained and the system BER performance increases significantly. Full article
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
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Article
No-Reference Objective Video Quality Measure for Frame Freezing Degradation
Sensors 2019, 19(21), 4655; https://doi.org/10.3390/s19214655 - 26 Oct 2019
Cited by 1 | Viewed by 1703
Abstract
In this paper we present a novel no-reference video quality measure, NR-FFM (no-reference frame–freezing measure), designed to estimate quality degradations caused by frame freezing of streamed video. The performance of the measure was evaluated using 40 degraded video sequences from the laboratory for [...] Read more.
In this paper we present a novel no-reference video quality measure, NR-FFM (no-reference frame–freezing measure), designed to estimate quality degradations caused by frame freezing of streamed video. The performance of the measure was evaluated using 40 degraded video sequences from the laboratory for image and video engineering (LIVE) mobile database. Proposed quality measure can be used in different scenarios such as mobile video transmission by itself or in combination with other quality measures. These two types of applications were presented and studied together with considerations on relevant normalization issues. The results showed promising correlation values between the user assigned quality and the estimated quality scores. Full article
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
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Article
Anisotropic Diffusion Based Multiplicative Speckle Noise Removal
Sensors 2019, 19(14), 3164; https://doi.org/10.3390/s19143164 - 18 Jul 2019
Cited by 8 | Viewed by 1480
Abstract
Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. [...] Read more.
Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images. Full article
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
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Article
A Dark Target Detection Method Based on the Adjacency Effect: A Case Study on Crack Detection
Sensors 2019, 19(12), 2829; https://doi.org/10.3390/s19122829 - 25 Jun 2019
Cited by 3 | Viewed by 1548
Abstract
Dark target detection is important for engineering applications but the existing methods do not consider the imaging environment of dark targets, such as the adjacency effect. The adjacency effect will affect the quantitative applications of remote sensing, especially for high contrast images and [...] Read more.
Dark target detection is important for engineering applications but the existing methods do not consider the imaging environment of dark targets, such as the adjacency effect. The adjacency effect will affect the quantitative applications of remote sensing, especially for high contrast images and images with ever-increasing resolution. Further, most studies have focused on how to eliminate the adjacency effect and there is almost no research about the application of the adjacency effect. However, the adjacency effect leads to some unique characteristics for the dark target surrounded by a bright background. This paper utilizes these characteristics to assist in the detection of the dark object, and the low-high threshold detection strategy and the adaptive threshold selection method under the assumption of Gaussian distribution are designed. Meanwhile, preliminary case experiments are carried out on the crack detection of concrete slope protection. Finally, the experiment results show that it is feasible to utilize the adjacency effect for dark target detection. Full article
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
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Article
Multi-View Image Denoising Using Convolutional Neural Network
Sensors 2019, 19(11), 2597; https://doi.org/10.3390/s19112597 - 07 Jun 2019
Cited by 6 | Viewed by 1450
Abstract
In this paper, we propose a novel multi-view image denoising algorithm based on convolutional neural network (MVCNN). Multi-view images are arranged into 3D focus image stacks (3DFIS) according to different disparities. The MVCNN is trained to process each 3DFIS and generate a denoised [...] Read more.
In this paper, we propose a novel multi-view image denoising algorithm based on convolutional neural network (MVCNN). Multi-view images are arranged into 3D focus image stacks (3DFIS) according to different disparities. The MVCNN is trained to process each 3DFIS and generate a denoised image stack that contains the recovered image information for regions of particular disparities. The denoised image stacks are then fused together to produce a denoised target view image using the estimated disparity map. Different from conventional multi-view denoising approaches that group similar patches first and then perform denoising on those patches, our CNN-based algorithm saves the effort of exhaustive patch searching and greatly reduces the computational time. In the proposed MVCNN, residual learning and batch normalization strategies are also used to enhance the denoising performance and accelerate the training process. Compared with the state-of-the-art single image and multi-view denoising algorithms, experiments show that the proposed CNN-based algorithm is a highly effective and efficient method in Gaussian denoising of multi-view images. Full article
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
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Article
DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation
Sensors 2019, 19(5), 1032; https://doi.org/10.3390/s19051032 - 28 Feb 2019
Cited by 7 | Viewed by 1651
Abstract
In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For [...] Read more.
In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For the latter, intermediate cues, such as 3D object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by CNN in the first stage. Object pose can then be solved by correspondence constraints constructed with these intermediate cues. In this paper, we focus on the postprocessing of a two-stage pipeline and propose to combine two learning concepts for estimating object pose under challenging scenes: projection grouping on one side, and correspondence learning on the other. We firstly employ a local-patch based method to predict projection heatmaps which denote the confidence distribution of projection of 3D bounding box’s corners. A projection grouping module is then proposed to remove redundant local maxima from each layer of heatmaps. Instead of directly feeding 2D–3D correspondences to the perspective-n-point (PnP) algorithm, multiple correspondence hypotheses are sampled from local maxima and its corresponding neighborhood and ranked by a correspondence–evaluation network. Finally, correspondences with higher confidence are selected to determine object pose. Extensive experiments on three public datasets demonstrate that the proposed framework outperforms several state of the art methods. Full article
(This article belongs to the Special Issue Advance and Applications of RGB Sensors)
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