Image deblurring is a widely used technique in image restoration, that helps to recover fine details lost due to blur. In this paper, we conduct an investigation on hybridizing diverse deblurring approaches‒Wiener filtering and Lucy-Richardson deconvolution, to find a better alternative that improves the sharpness and clarity of blurred images. Wiener filtering is a one-step algorithm based on the frequency domain and is robust to Gaussian noise. The Lucy-Richardson deconvolution algorithm, on the other hand, is iteratively applied in the spatial domain to reduce the variation between the original and restored image; it is robust to low-level lighting conditions and Poisson-type of noise. The proposed hybrid method combines the strengths of filtering and deconvolution to create a more robust deblurring technique for restoring the quality of the degraded image. Furthermore, the effect of applying histogram equalization as a post-processing step on deblurred images is examined. Experiments on the GoPro dataset prove that the hybrid Wiener–Lucy-Richardson strategy outperforms the baseline methods in terms of high peak signal-to-noise ratio (29.845 dB) and high structural similarity index measure (0.7035) which indicates that the restored image is closer in quality to the original sharp image.