An efficient reversible data hiding using SVD over a novel weighted iterative anisotropic total variation based denoised medical images

Diwakar, Manoj; Kumar, Pardeep; Singh, Prabhishek; Tripathi, Amrendra; Singh, Laxman

doi:10.1016/j.bspc.2022.104563

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…Total variation denoising is a regularization technique used to reduce noise in biomedical images while preserving edges. It minimizes the total variation in pixel values within the image, effectively smoothing the image while maintaining sharp boundaries [53].…”

Section: Filtering Methods For Biomedical Image Denoisingmentioning

confidence: 99%

Filtering Methods for Biomedical Image Denoising

Zhao

2023

Preprint

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In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

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Section: Filtering Methods For Biomedical Image Denoisingmentioning

confidence: 99%

Filtering Methods for Biomedical Image Denoising

Zhao

2023

Preprint

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“…However, unlike steganography and digital watermarking, RDH enables reversibility and can losslessly recover the original carrier. Due to the lossless recovery property, RDH can be used as authentication protection for active forensics, or for covert communication and integrity authentication for medical and military images [6,7].…”

Section: Introductionmentioning

confidence: 99%

High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Li,

Xiao

et al. 2023

Mathematics

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Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding data into visually insensitive regions (e.g., edges and textures). In this paper, following this direction, a high-capacity RDH approach based on multi-embedding is proposed. The cover image is decoupled to select the embedding sequence that can better utilize texture pixels and reduce the size of the reconstruction information, and a multi-embedding strategy is proposed to embed the secret data along with the reconstruction information by matrix embedding. The experimental results demonstrate that the proposed method provides a superior visual quality and higher embedding capacity than some state-of-the-art RDH works for binary images. With an embedding capacity of 1000 bits, the proposed method achieves an average PSNR of 49.45 dB and an average SSIM of 0.9705 on the test images. This marks an improvement of 1.1 dB in PSNR and 0.0242 in SSIM compared to the latest state-of-the-art RDH method.

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Optimize multiscale feature hybrid-net deep learning approach used for automatic pancreas image segmentation

Paithane

2024

Machine Vision and Applications

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An efficient reversible data hiding using SVD over a novel weighted iterative anisotropic total variation based denoised medical images

Cited by 11 publications

References 27 publications

Filtering Methods for Biomedical Image Denoising

Filtering Methods for Biomedical Image Denoising

High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Optimize multiscale feature hybrid-net deep learning approach used for automatic pancreas image segmentation

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