Pith. sign in

REVIEW 4 major objections 6 minor 30 references

An Image Processing Based Blur Reduction Technique in Smartphone-to-Smartphone Visible Light Communication System

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper proposes a receiver-side blur-reduction pipeline for smartphone-to-smartphone visible light communication and reports that it improves recovery efficiency to 96% across varied distance, tilt, rotation, and lighting conditions.

desk verdict A systematic but under-specified experimental report whose headline 96% recovery-efficiency claim is not defined or reproducible; the algorithm itself is not new. read the letter →

arxiv 2506.23002 v1 pith:N47ISRA4 submitted 2025-06-28 eess.IV

Vaigai Nayaki Yokar , Hoa Le-Minh , Zabih Ghassemlooy , Wai Lok Woo This is my paper · ORCID
classification eess.IV
keywords visiblelightcommunicationopticalcamerasmartphone-to-smartphoneimageblurreductiongrayscaleconversionadaptivethresholdingOOKQRcode
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper is trying to establish that a simple, low-complexity image processing chain—grayscale conversion, contrast enhancement, scaling, and adaptive binarization—can rescue blurred frames in smartphone-to-smartphone visible light communication (S2SVLC) instead of discarding them. If true, the system recovers 96% of transmitted bits at the receiver across distances up to 50 cm, tilt and rotation spans, and both ambient and dark surroundings, while also supporting a higher data rate. The motivation is practical: S2SVLC links based on screen-to-camera optical communication suffer blur from distance, misalignment, and lighting, and existing restoration methods are either too slow or need noise statistics unavailable at the receiver. The paper tests the pipeline with ASCII and QR coding using a real phone-to-phone setup and reports lower bit-error rates than the conventional no-image-processing system.

What carries the argument

The load-bearing mechanism is the blur reduction algorithm's own pipeline, implemented at the receiver. Step 1 maps RGB to grayscale, either by equal average or weighted luminance, reducing leakage between color channels. Step 2 applies contrast stretching so dark and bright cells separate. Step 3 normalizes minimum and maximum pixel values to set a detection threshold. Step 4 uses adaptive thresholding, which chooses per-region thresholds, to binarize the image into white and black cells that are then converted back into OOK bits. The central decision is a thresholding decision: each cell's value after scaling is compared to an adaptively chosen level, turning a blurred analog image into a clean binary representation without knowing the noise variance.

What would settle it

Transmit a known 40,000-bit sequence, apply the four-step pipeline, and compare the decoded bits to the original using an independent synchronization method that does not use the decoded payload to find the alignment; if the 96% recovery efficiency drops substantially under this check, the central claim fails.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that blur in S2SVLC can be largely corrected by a four-step receiver-side algorithm: convert the captured RGB frame to a single-channel grayscale image, apply contrast stretching, normalize the gray-level range, and binarize with adaptive thresholding. Because the transmitter encodes each bit as a white or black cell in an MxN frame under OOK-NRZ, the binarized output can be read as a bit stream. The paper reports that this processing improves recovery efficiency to about 96% at the receiver, lowers bit-error rate in ambient and no-light conditions over a 0–50 cm link span, and keeps bit-error rate below the conventional system across tilt and rotation experiments. The paper also argues that the method is less complex than HSV-based systems and non-blind or blind restoration, because it avoids repeated scanning and does not need noise statistics or high-computation networks. The claim covers both ASCII and QR encoded data.

Load-bearing premise

The numbers assume the receiver can reliably synchronize and align each captured frame with the transmitted 40,000-bit sequence before errors are counted; the paper does not describe that alignment procedure, so the reported error rates and the 96% figure depend on an unstated synchronization step.

Editorial extensions

If this is right

  • The proposed pipeline can be applied to standard QR and ASCII encoded frames, so it does not require customized barcodes like earlier systems.
  • Bit-error rate decreases in both ambient-light and dark-room conditions compared with the conventional receiver, and the gain persists across the tested 0–50 cm distance range.
  • Because blurred frames are processed rather than discarded, fewer retransmissions or rescans are needed, which the paper says raises the achievable data rate.
  • Recovery efficiency reaches about 96%, meaning only a small fraction of the transmitted bits is lost at the receiver across the tested conditions.
  • The method's low complexity makes it more feasible for real-time smartphone processing than HSV-based or neural restoration approaches.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 96% recovery-efficiency figure is only as strong as the unstated frame-synchronization and bit-to-cell alignment step; a reader should treat it as an upper bound on decoding accuracy until that step is specified.
  • A natural testable extension is to apply the same four-step pipeline to LED-to-camera links, where rolling-shutter blur and cell-boundary smearing are analogous but the transmitter is not a screen.
  • One could also benchmark the pipeline against learned deblurring on the same captured frames; the paper's complexity argument would be strengthened if the simple pipeline matched or beat learned methods on bit-error rate per frame.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a low-complexity blur reduction technique for smartphone-to-smartphone visible light communication (S2SVLC). The receiver-side algorithm converts a captured RGB image to grayscale, applies contrast enhancement, scaling, and adaptive thresholding, and claims this reduces bit errors compared to a conventional system without image processing. Experiments vary link distance (0–50 cm), tilt angle (−50° to 50°), rotation angle (−40° to 40° in Section VI, though Table I says −50° to 50°), and illumination (ambient vs. dark), transmitting 40,000 bits encoded as a 200×200 cell image. The abstract and conclusion state that the technique 'improve[s] the recovery efficiency to 96% at the receiver end at different conditions.' The central claim is that the algorithm substantially reduces BER across these conditions, but the paper provides no definition of the 96% metric, no synchronization or bit-to-cell mapping procedure, and no statistical characterization of the BER measurements.

Significance. If the 96% recovery-efficiency claim were properly defined and supported, the work would offer a practically attractive, low-complexity pre-processing step for smartphone-based optical camera communication, a topic of ongoing interest in the VLC community. The paper presents a real measurement campaign over distance, tilt, rotation, and lighting, which is appropriate for a systems paper. However, the contribution as written is not reproducible: the headline metric is undefined and internally inconsistent with the reported BER values, the BER computation pipeline lacks a documented synchronization/alignment stage, and the comparison baseline is not quantified. The paper also relies heavily on the authors' own prior publications for the core algorithm steps without clearly delineating the new contribution. These issues are load-bearing for the central claim, and I cannot recommend acceptance or even major revision without substantial rework.

major comments (4)
  1. [Abstract; Section VI, Figs. 5, 7, 9; Conclusion] The paper's central claim—that the proposed technique improves 'recovery efficiency to 96%'—is never defined. No equation or text in Sections V–VII defines what 'recovery efficiency' means (e.g., 1−BER, fraction of recovered bits, or fraction of frames decoded). The reported BER values at tilt extremes and rotation extremes are approximately 0.385 (Section VI, Fig. 7) and 0.35 (Section VI, Fig. 9), respectively; if efficiency is 1−BER, these correspond to 61.5% and 65%, not 96%. No baseline BER is reported for the 'conventional system' against which the 96% improvement is computed. The headline claim is therefore neither definable nor derivable from the data presented.
  2. [Section V (Steps 1–4); Table I; Section VI] The paper never describes how the binarized image is mapped back to the transmitted 40,000-bit sequence. The receiver captures a photograph of a 200×200 cell frame; with varying distance, tilt, and rotation, the cell grid undergoes perspective distortion. The algorithm description in Section V stops at adaptive thresholding and provides no grid detection, perspective correction, or frame synchronization step. Without a documented alignment procedure, the BER values in Figures 5, 7, and 9 cannot be reproduced, and may measure misregistration rather than decoding errors. This is a fundamental reproducibility gap for the paper's only quantitative performance evidence.
  3. [Section VI; Figures 5, 7, 9] The BER results are reported without any statistical support: no error bars, no number of repeated trials, and no confidence intervals. The text also contains contradictory phrasing: for tilt and rotation, it states that 'the BER increases with the increasing tilt/rotation angles' but then says 'The bit error rate reduces to around 0.385' and 'reduces to around 0.35.' The intended meaning is presumably that the BER rises to about 0.385/0.35 at the extreme angles, but the wording is ambiguous. In addition, no quantitative BER values for the 'conventional system' are given in the figures or text, so the claimed improvement over baseline cannot be verified.
  4. [Section II; References [4]–[7]] The novelty boundary of the proposed algorithm relative to the authors' own prior work is not articulated. Section II introduces the proposed method as 'novel,' but the algorithm steps in Section V cite the authors' previous publications [4] and [5] ('Data Detection Technique' and 'Non-Blind Image Restoration Technique') without specifying what is new in this manuscript. Since the paper's contribution rests on the algorithm, the reader cannot determine whether this is an incremental extension of prior work or a genuinely new technique. The manuscript should clearly separate the new contribution from the previously published components.
minor comments (6)
  1. [Abstract] The sentence 'The key technique it to avoid the repeated scanning of the transmitted data' contains a grammatical error ('it to' should be 'is to').
  2. [Section IV] Equation numbering is inconsistent: two equations are labeled (4), and the text refers to 'In (4)' for the contrast-stretch formula while Equation (2) and (3) appear earlier. The variables a, b, c, d in the contrast-stretch equation are introduced but their relationship to the text is confusing.
  3. [Figure numbering] Several figure references do not match the captions: Section V.A refers to 'Fig. 2' for the received blurred image but the actual system block diagram is Figure 2; Section VI first paragraph refers to 'Fig 4' for the real-time setup, but the real-time setup is Figure 3; and the schematic in Figure 4 is labeled 'Fig. 4' in the text as BER vs distance. Please renumber figures or correct in-text references.
  4. [Section VI; Table I] The rotation range is inconsistent: Table I states rotation angles of '-50 t0 50 degrees' (with a typo 't0'), while the rotation experiment in Section VI says '-40 to 40 degrees' and later mentions '(-50,50) degrees'. Clarify the actual experimental range.
  5. [References] The reference list has multiple numbering errors: [5] is listed as a non-blind restoration paper but is cited as TETRIS in the text; [6] is cited as the flashing/surfing method but the reference list entry is about hemoglobin estimation; [13] is used for both SoftLight and a color-spaces paper; [22] is duplicated. These errors impede the reader's ability to trace the literature.
  6. [Section VI] The description of the 'no light' condition is incomplete: only the ambient case is quantified (123 lumens), and the dark-room condition (e.g., residual screen illumination, any standby lighting) is not specified, which affects reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the 96% improvement claim is an empirical BER result, not a quantity constructed from the paper's own definitions.

full rationale

The paper's core contribution is an image-processing pipeline (grayscale conversion, contrast stretching, scaling, adaptive thresholding) applied to captured screen-to-camera frames, followed by BER measurements. No derived quantity is defined in terms of the target result. The 96% recovery-efficiency figure is reported from the experimental comparison, and although its relationship to the plotted BER values is unexplained, that is a reproducibility or correctness concern rather than a circular one. The paper cites its authors' prior works extensively, particularly [4] for thresholding and [7] for an earlier blur-reduction technique, but those citations support descriptions of standard processing steps that are also stated in this paper; they are not uniqueness theorems, fitted parameters, or definitions that force the claimed conclusion. No fitting step enters the BER computation, no parameter is extracted from a subset of data and then renamed a prediction, and no mathematical result is imported from a same-author citation to exclude alternatives. The central claim therefore stands or falls on the reported measurements and the missing synchronization and bit-mapping details, which are evidentiary issues, not circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities. The free parameters are algorithm tuning values that are not disclosed, and the axioms are standard assumptions about image processing and modulation plus an unverified assumption about camera behavior. The central claim rests mainly on the undisclosed experimental parameters and the assumption that BER alignment was performed correctly.

free parameters (1)
  • Adaptive threshold and contrast stretch parameters = not reported
    The algorithm uses adaptive thresholding and a contrast stretch that 'by default, 1% of the data is saturated', but the actual window size, threshold logic, and stretch limits used in the experiments are not stated. The reported BER values depend on these choices.
assumptions (3)
  • domain assumption Grayscale conversion and binarization preserve the encoded black and white cells well enough for decoding.
    Section V assumes that after grayscale, contrast enhancement, and thresholding, the transmitted 0/1 cells can be recovered with acceptable fidelity. This is plausible for clean images but is not proven in the paper.
  • standard math The OOK-NRZ signal model (equation (1)) describes the screen-to-camera link.
    Section IV introduces the OOK-NRZ envelope as the modulation model. This is a standard assumption in visible light communication and is not the source of the paper's central claim.
  • domain assumption The camera's automatic focus and exposure behavior is consistent across ambient and no-light conditions.
    Table I lists camera parameters but does not state whether focus and exposure were locked. If auto-exposure varied with lighting, the BER differences between conditions could be an artifact of camera settings rather than the algorithm.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An Image Processing Based Blur Reduction Technique in Smartphone-to-Smartphone Visible Light Communication System." pith.science (2026). https://pith.science/paper/N47ISRA4

@misc{pith2026250623002,
  author       = {Pith},
  title        = {Pith review of: An Image Processing Based Blur Reduction Technique in Smartphone-to-Smartphone Visible Light Communication System},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N47ISRA4}},
  note         = {Machine review of arXiv:2506.23002}
}
read the original abstract

In this paper, we present a blur reduction technique for smartphone-to-smartphone visible light communications (S2SVLC). The key technique it to avoid the repeated scanning of the transmitted data and to lower the amount of data discarded at the receiver end of the S2SVLC system. This image processing method will improve the system recognition efficiency and data rate. The proposed method includes converting the red-green-blue (RGB) image into grayscale, applying contrast enhancement, scaling and binarizing the image to reduce the blur levels in the image. The experiment includes practical data acquisition and further processing and estimation in MATLAB. The experiment is carried out in different conditions like distance, rotation, and tilt also considering different surrounding illuminations like ambient light and no light conditions to estimate the blur levels in S2SVLC. In this experimental investigation two types of coding, American Standard code for information interchange (ASCII), and quick response (QR) code are used for data transmission in S2SVLC. The obtained results indicate that, the proposed technique is proven to improve the recovery efficiency to 96% in the receiver end at different conditions.

Figures

Figures reproduced from arXiv: 2506.23002 by the authors.

Figure 1
Figure 1. In S2SVLC, a stream of data (text of any other media [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: FIGURE 5 [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 4
Figure 4. Figure 4: FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 9
Figure 9. Figure 9: FIGURE 9 [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 29 canonical work pages

  1. [7]

    A Novel Blur Reduction Technique For QR And ASCII Coding In Smartphone Visible Light Communications,

    V. N. Yokar, Hoa-Le-Minh, F. Ghassemlooy, and W. L. Woo, “A Novel Blur Reduction Technique For QR And ASCII Coding In Smartphone Visible Light Communications,” in 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Jul. 2022, pp. 428–433. doi: 10.1109/CSNDSP54353.2022.9907993

    arXiv 2022

  2. [13]

    Rain Bar: Robust Application-Driven Visual Communication Using Color Barcodes

    Q. Wang, M. Zhou, K. Ren, T. Lei, J. Li and Z. Wang, "Rain Bar: Robust Application-Driven Visual Communication Using Color Barcodes", 2015 IEEE 35th International Conference on Distributed Computing Systems, pp. 537-546, 2015

    work page 2015

  3. [4]

    Data Detection Technique for Screen -to-Camera Based Optical Camera Communications,

    V. N. Yokar, H. Le -Minh, Z. Ghassemlooy, and W. L. Woo, “Data Detection Technique for Screen -to-Camera Based Optical Camera Communications,” presented at the 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Proces sing (CSNDSP), IEEE, 2024, pp. 233–237

    work page 2024

  4. [5]

    Non-Blind Image Restoration Technique in Screen –to– Camera based Optical Camera Communications,

    V. N. Yokar, H. Le-Minh, L. N. Alves, S. Zvanovec, W. L. Woo, and Z. Ghassemlooy, “Non-Blind Image Restoration Technique in Screen –to– Camera based Optical Camera Communications,” presented at the 2024 7th International Balkan Conference on Communicatio ns and Networking (BalkanCom), IEEE, 2024, pp. 101–106. 8 VOLUME XX, 2022

    work page 2024

  5. [1]

    Smartphone Beam Profile in a Screen -to-Camera- Based Optical Communication System,

    V. N. Yokar et al., “Smartphone Beam Profile in a Screen -to-Camera- Based Optical Communication System,” in 2023 17th International Conference on Telecommunications (ConTEL), Jul. 2023, pp. 1 –6. doi: 10.1109/ConTEL58387.2023.10199032

    work page arXiv 2023

  6. [2]

    Performance evaluation technique for screen -to-camera-based optical camera communications,

    V. N. Yokar, H. Le -Minh, Z. Ghassemlooy, and W. L. Woo, “Performance evaluation technique for screen -to-camera-based optical camera communications,” IET Optoelectronics, vol. n/a, no. n/a, Aug. 2023, doi: 10.1049/ote2.12102

    work page doi:10.1049/ote2.12102 2023

  7. [3]

    Fast Link Recovery via PTP-synchronized Nanosecond Optical Switching

    V. Yokar et al., “Fast Link Recovery via PTP-synchronized Nanosecond Optical Switching,” arXiv preprint arXiv:2412.13778, 2024

    work page Pith review arXiv 2024

  8. [6]

    Hemoglobin Estimation through Image Processing of Eye Image via Determination of the Density Parameter of a 2 -D Wavelet Processed Image

    V. N. Yokar, “Hemoglobin Estimation through Image Processing of Eye Image via Determination of the Density Parameter of a 2 -D Wavelet Processed Image”

    work page

Show all 30 references
  1. [8]

    Future Optical Camera Communication Based Application and Opportunities for 5G and beyond

    Shahjalal, Moh Khalid Hasan, Mostafa Zaman Chowdhury and Yeong Min Jang, "Future Optical Camera Communication Based Application and Opportunities for 5G and beyond", 2019 International conference on Artificial Intelligence in Information and communication, Feb 2019

    2019

  2. [9]

    An Approach for Securing QR code using Cryptography and Visual Cryptography,

    C. Bhardwaj, H. Garg and S. Shekhar, "An Approach for Securing QR code using Cryptography and Visual Cryptography," 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 2022, pp. 284-288, doi: 10.1109/CISES54857.2022.9844332

    2022

  3. [10]

    QR Code Scanning app for Mobile Devices

    M. Moisoiu, A. Negrău, R. Győrödi, C. Győrödi and G. Pecherle, "QR Code Scanning app for Mobile Devices", International Journal of Computer Science and Mobile Computing, vol. 3, no. 6, pp. 334-341, 2014

    2014

  4. [11]

    A Blur Equalization Method for Screen-to-Camera Based Optical Camera Communications,

    C. Lin et al., "A Blur Equalization Method for Screen-to-Camera Based Optical Camera Communications," 2019 18th International Conference on Optical Communications and Networks (ICOCN), 2019, pp. 1-3, doi: 10.1109/ICOCN.2019.8934224

    2019

  5. [12]

    COBRA:Color Barcode Streaming for Smartphone System

    Tian Hao, Ruogu Zhou and Guollang Xing, "COBRA:Color Barcode Streaming for Smartphone System", In MobiSys ’12: Proceedings of the 10th International conference on Mobile System application and services, pp. 85-98, 2012

    2012

  6. [14]

    SoftLight: Adaptive visible light communication over screen-camera links

    W. Du, J. C. Liando and M. Li, "SoftLight: Adaptive visible light communication over screen-camera links", IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1-9, 2016

    2016

  7. [15]

    Color spaces Advantages and disadvantages in image color clustering Segmantation

    Edgar Chavolla, Daniel Zaldivar, Erik Cuevas and Marco A. Pérez Cisneros, "Color spaces Advantages and disadvantages in image color clustering Segmantation", Studies in computational intelligence, 2018

    2018

  8. [16]

    Removal of blur in images based on least squares solutions

    I. Stojanovic, P. Stanimirovic, S. Chountasis et al., "Removal of blur in images based on least squares solutions", Mathematical Methods in the Applied Sciences, vol. 36, no. 17, pp. 2280-2296, 2013

    2013

  9. [17]

    Motion-blurred image restoration using modified inverse all-pole filters

    B. Likhterov and N. S. Kopeika, "Motion-blurred image restoration using modified inverse all-pole filters", Journal of Electronic Imaging, pp. 13, 2004

    2004

  10. [18]

    Handling motion blur in multi-frame super-resolution

    Z. Ma, R. Liao, X. Tao et al., "Handling motion blur in multi-frame super-resolution", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

    2015

  11. [19]

    IEEE Computer Society 2011 IEEE Standard for Local and Metropolitan Area Networks - Part 15.7: ShortRange Wireless Optical Communication Using Visible Light (USA: IEEE Computer Society)

    2011

  12. [21]

    Android Developers

    "Android Developers", Developer.android.com, Oct. 2023

    2023

  13. [22]

    An Overview of TFT LCD Display Technology

    Putman and H. Peter, "An Overview of TFT LCD Display Technology", In SMPTE Technical Conference and Exhibition 147th, pp. 1-5, 2005

    2005

  14. [23]

    Comparative Study of Image Processing Performance of Camera- Based Visible Light Communication Using Android Acceleration Frameworks

    H. Dzieciol, H. L. Minh, Z. Ghassemlooy, P. T. Dat and S. T. Tran, "Comparative Study of Image Processing Performance of Camera- Based Visible Light Communication Using Android Acceleration Frameworks", 2018 11th International Symposium on Communication Systems Networks & Digi...

    2018

  15. [24]

    Comparison of modern CCD and CMOS image sensor technologies and systems for low resolution imaging

    Carlson, Bradley S. "Comparison of modern CCD and CMOS image sensor technologies and systems for low resolution imaging." In Sensors, 2002. Proceedings of IEEE, vol. 1, pp. 171-176. IEEE, 2002

    2002

  16. [25]

    Association, Signal Infrared Physical Layer Specification , Version 1.4, 2001

    I.D. Association, Signal Infrared Physical Layer Specification , Version 1.4, 2001

    2001

  17. [26]

    Design and Implementation of Color- Shift Keying for Visible Light Communications,

    E. Monteiro and S. Hranilovic, "Design and Implementation of Color- Shift Keying for Visible Light Communications," J. Lightwave Technol. 32, 2053-2060 (2014)

    2014

  18. [27]

    Hu, Pengfei & Pathak, Parth & Zhang, Huanle & Yang, Zhicheng & Mohapatra, Prasant. (2019). High Speed LED-to-Camera Communication using Color Shift Keying with Flicker Mitigation. IEEE Transactions on Mobile Computing. PP. 1-1. 10.1109/TMC.2019.2913832

    2019

  19. [28]

    Mathworks.com (Oct 2022)

    2022

  20. [29]

    PLOS ONE7(1):e29740

    Kanan C, Cottrell GW (2012) Color-to-Grayscale: Does the Method Matter in Image Recognition?. PLOS ONE7(1):e29740. https://doi.org/10.1371/journal.pone.0029740

    2012 doi

  21. [30]

    Optical Camera Communication for IoT-Rolling – Shutter Based MIMO scheme with Grouped LED Array based transmitter

    Shivani Rajendra Teli, Vicente Matus, Stanislav Zvanovec, Rafael Perez Jimenez, Stanislav Vitek and Zabih Ghassemlooy, "Optical Camera Communication for IoT-Rolling – Shutter Based MIMO scheme with Grouped LED Array based transmitter", Visible light communication Networking an...

    2020

  22. [31]

    Blurred Image Enhancement Using Contrast StretchingLocal Edge Detection and Blind Deconvolution

    Prasad Nagelli, C. Lokanath Reddy and B.T.R. Naresh Reddy, "Blurred Image Enhancement Using Contrast StretchingLocal Edge Detection and Blind Deconvolution", In the International Journal of Information and Computation Technology, vol. 4, no. 3, pp. 247-252, 2014

    2014

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.