Browsing by Author "Gupta, H."

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    Adaptive conductance function based improved diffusion filtering and bi-dimensional empirical mode decomposition based image denoising
    (Springer, 2023) Gupta, H.; Singh, H.; Kumar, A.; Vishwakarma, A.
    This paper presents a new method for image denoising based on a two-dimensional empirical mode decomposition algorithm and semi-adaptive diffusion coefficient in anisotropic diffusion filter. The proposed model uses a local difference value method to compare and replace some pixels of the noisy image with a pre-processed image that has been passed through a Gaussian filter. A bi-dimensional empirical mode decomposition algorithm is then employed to decompose the noise-contaminated image into its intrinsic mode functions in which high-frequency and low-frequency noise components are removed by applying a diffusion filter. The filter has a semi-adaptive threshold in the diffusion coefficient with parameters like connectivity, conductance function, number of iterations, and gradient threshold. The semi-adaptive threshold for each diffusion is implemented by introducing gradient values in the threshold of the corrupted image. The image is then reconstructed from these denoised intrinsic mode functions. The performance of the proposed method is assessed in terms of peak signal-to-noise ratio, mean square error, and structural similarity index and is compared with the existing methodologies. The results obtained from experimentation indicate that the proposed method is efficient in both feature retention and noise suppression. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Variational mode decomposition based image denoising using semi-adaptive conductance function inspired diffusion filtering
    (Springer, 2024) Gupta, H.; Singh, H.; Kumar, A.; Vishwakarma, A.; Singh, G.K.
    In day-to-day life, images are the most frequent and casual way of information sharing. These images are susceptible to external disturbances or noise. Thus, to curb noise, image denoising algorithms are utilized. In this paper, the variational mode decomposition, with its concurrent and a non-recursive process for determining the mode functions that also provides a robust method for image denoising, has been introduced. This decomposition process divides the whole spectrum of the signal into a number of sub-bands or mode functions, centered around their respective center frequencies. To these mode functions, spatial filters such as bilateral filter, wiener filter, and modified anisotropic diffusion filter are employed. These filters help in enhancing the yield of the quality assessment metrics; such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), together with the semi-adaptive conductance function in the diffusion filter. The parameters of these respective spatial filters are calibrated, and then selected in order to get the best possible metric scores. The applicability and ability of the algorithm to suppress noise are compared visually and quantitatively for the noisy image using modified variational mode decomposition and other denoising algorithms in both low and high noise levels. The algorithm provides an average decrease of 62% in case of MSE, 28% increase in PSNR, and 110% increase in SSIM when compared with other denoising techniques. The estimated metric score values signify that the proposed method has a better prospect as a denoising algorithm. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.