Pratap Singh R.Kumar Singh M.2025-01-132025-01-13202413682199https://dl.bhu.ac.in/ir/handle/123456789/3313In 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. � 2024 The Royal Photographic Society.endeblurringExpectation-maximizationhard thresholdingimage deconvolutionregularizationrisk analysissoft thresholdingwaveletArticle10.1080/13682199.2024.2349340