Microscopic cell image analysis is indispensable to cell biology. Images of cells can easily degrade due to optical diffraction or focus shift, as this results in low signal-to-noise ratio (SNR) and poor image quality, hence affecting the accuracy of cell analysis and identification. For a quantitative analysis of cell images, restoring blurred images to improve the SNR is the first step. A parameter estimation method for defocused microscopic cell images based on the power law properties of the power spectrum of cell images is proposed. The circular radon transform (CRT) is used to identify the zero-mode of the power spectrum. The parameter of the CRT curve is initially estimated by an improved differential evolution algorithm. Following this, the parameters are optimized through the gradient descent method. Using synthetic experiments, it was confirmed that the proposed method effectively increased the peak SNR (PSNR) of the recovered images with high accuracy. Furthermore, experimental results involving actual microscopic cell images verified that the superiority of the proposed parameter estimation method for blurred microscopic cell images other method in terms of qualitative visual sense as well as quantitative gradient and PSNR.