Dr. Ravi Pratap Singh
@bhu.ac.in
Scopus Publications
- An Efficient Hybrid Threshold for Image Deconvolution in Expectation Maximization Framework
Ravi Pratap Singh, Manoj Kumar Singh
Circuits Systems and Signal Processing, 2025 - Image deconvolution using hybrid threshold based on modified L1-clipped penalty in EM framework
Ravi Pratap Singh, Manoj Kumar Singh
Signal Processing, 2025 - Hybrid Thresholding for Image Deconvolution in Expectation Maximization framework
Ravi Pratap Singh, Manoj Kumar Singh
Imaging Science Journal, 2025
In this study, we proposed an image deconvolution method in the expectation maximization (EM) framework. This method involves two steps: (i) E-step: utilizing the fast Fourier transform (FFT) for computationally efficient inversion of the convolution operator and (ii) M-step: employing the discrete wavelet transform (DWT) for estimating the original image from the image obtained in the E-step. In M-step, we proposed a modified L1-clipped penalty resulting in a hybrid thresholding scheme that integrates conventional hard and soft thresholds. This hybrid threshold ameliorates the inherent bias-variance trade-offs associated with traditional hard and soft thresholding schemes. The mathematical expressions for the risk, bias, and variance of the proposed hybrid threshold are derived and the performance is evaluated through simulation. Experimental results show that the proposed method achieves optimal values for variance and bias, thereby minimizing the risk. Moreover, the proposed method outperforms state-of-the-art methods in terms of performance metrics: PSNR, ISNR, and SSIM. - Risk Minimization Approach for Image Restoration Using L2Penalty in EM Framework
Ravi Pratap Singh, Manoj Kumar Singh
2023 6th International Conference on Information Systems and Computer Networks Iscon 2023, 2023
This paper presents a modified expectation maximization (EM) technique for the restoring the image from its degraded model. The proposed methodology comprises the concept of minimization of the risk associated with the L2 penalty in the maximization step (M-step) of the algorithm. This method optimizes the worst case error, and at the same time also ensures the uniform shrinkage of the wavelet coefficients by using proper regularization parameter. The proposed methodology achieves 8.77 dB which is larger than the ISNR achieved by the state-of-the art methods.
