Nowadays, the usage of digital cameras in daily life has increased, leading to a growing demand for enhancing the quality of captured images. One of the most challenging problems in image processing is the restoration of blurred or out-of-focus images. This paper presents Enhancing grayscale image clarity through blind deconvolution and PSF estimation with total variation regularization. This blind deconvolution approach is used to handle the problem of removing blur from acquired photos, making it easier to restore defocused grayscale photographs. Determining the ideal picture and the point spread function (PSF) may be challenging when using blind deconvolution, which aims to recreate a clear ideal image from a single fuzzy image. As a result, this work also introduces a unique PSF estimate technique that makes use of an adaptive filter and total variation regularization. Finally, the results obtained after implementation are demonstrated based on the blur type and performance metrics, which include the mean square error and the peak signal-to-noise ratio. The experimental outcomes indicate that our method is the best among various other methods in terms of deblurring performance.