Fast Computation of Hahn Polynomials for High Order Moments

Mahmmod, Basheera M.; Abdulhussain, Sadiq H.; Suk, Tomáš; Hussain, Abir Jaafar

doi:10.1109/access.2022.3170893

Cited by 40 publications

(35 citation statements)

References 31 publications

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“…These transforms include Discrete Hahn Polynomials (DHPs) which has been used for efficient feature extraction in image processing. The work done in [13] addressed some shortcomings and proposed efficient Hahn orthogonal basis to be deployed for higher orders. Similarly, other classes of orthogonal polynomial have also shown promising results in representing higher order signals in orthogonal basis for efficient feature extraction.…”

Section: A Sparsitymentioning

confidence: 99%

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

et al. 2022

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Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.

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Section: A Sparsitymentioning

confidence: 99%

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

et al. 2022

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“…[28] Fractional Fourier transform is also widely used for image encryption for many years [29,30]. Use of Krawtchouk polynomials [31], Meixner polynomials [32], Hahn polynomials [33] and Charlier polynomials [34] are being recently used by researchers nowadays for feature extraction from images.…”

Section: Introductionmentioning

confidence: 99%

Efficient Storage and Encryption of 32-Slice CT Scan Images Using Phase Grating

Patra

Saha²,

Bhattacharya

2022

Arab J Sci Eng

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Medical images are treated as sensitive as it carries patients' confidential information and hence must be protected from unauthorized access. So, a strong encryption mechanism is a primary criterion to transmit these images over the internet to protect them from intruders. In many existing algorithms, noise affection in the extracted images is high, hence not suitable for medical data encryption. Here, we present a new method using phase grating to multiplex as well as encrypting 32 cross-sectional CT scan images (slices) in a single canvas for optimization of storage space and improvement of security. The entire process is divided into a few steps. Before transmission, the main canvas is encrypted with the help of a random phase matrix. The main canvas is further encrypted by the transposition method to enhance security. After decryption, inverse Fourier transform is applied at the proper location of the decrypted canvas to extract the images from the spectra. Quality is measured with peak-signal-to-noise ratio and correlation coefficient methods. Here, it is greater than 38 and the correlation coefficient is close to 1 for all images, thereby indicating of good quality of extracted images. The effect of three common cyber-attacks (viz. known-plaintext attack, chosen-plaintext attack, and chosen-ciphertext attack) is also presented here. The correlation coefficient during cyber-attacks is found to be close to zero, which implies the robustness of the algorithm against cyber-attacks. Finally, a comparison with existing techniques shows the effectiveness of the proposed method.

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“…For low-quality underwater original images, the traditional semantic segmentation methods have poor segmentation results because they cannot deal with complex underwater environment information [ 8 , 9 ]. To solve the above problems and improve the semantic segmentation effect of underwater images, this paper proposes a new encoder-decoder network structure for the semantic segmentation of underwater images.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation method of underwater images based on encoder-decoder architecture

Wang

Shao

et al. 2022

PLoS ONE

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With the exploration and development of marine resources, deep learning is more and more widely used in underwater image processing. However, the quality of the original underwater images is so low that traditional semantic segmentation methods obtain poor segmentation results, such as blurred target edges, insufficient segmentation accuracy, and poor regional boundary segmentation effects. To solve these problems, this paper proposes a semantic segmentation method for underwater images. Firstly, the image enhancement based on multi-spatial transformation is performed to improve the quality of the original images, which is not common in other advanced semantic segmentation methods. Then, the densely connected hybrid atrous convolution effectively expands the receptive field and slows down the speed of resolution reduction. Next, the cascaded atrous convolutional spatial pyramid pooling module integrates boundary features of different scales to enrich target details. Finally, the context information aggregation decoder fuses the features of the shallow network and the deep network to extract rich contextual information, which greatly reduces information loss. The proposed method was evaluated on RUIE, HabCam UID, and UIEBD. Compared with the state-of-the-art semantic segmentation algorithms, the proposed method has advantages in segmentation integrity, location accuracy, boundary clarity, and detail in subjective perception. On the objective data, the proposed method achieves the highest MIOU of 68.3 and OA of 79.4, and it has a low resource consumption. Besides, the ablation experiment also verifies the effectiveness of our method.

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Fast Computation of Hahn Polynomials for High Order Moments

Cited by 40 publications

References 31 publications

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

Efficient Storage and Encryption of 32-Slice CT Scan Images Using Phase Grating

Semantic segmentation method of underwater images based on encoder-decoder architecture

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