Blur and noisy image restoration for near real time applications

Image restoration of blur and noisy images can be performed in either of the two ways i.e. denoising after deblurring or deblurring after denoising. While performing deblurring after denoising, the residual noise is greatly amplified due to the subsequent deblurring process. In case of denoising after deblurring, the denoising stage severely blurs the image and leads to inadequate restoration. Denoising can be done mainly in two ways namely, linear filtering and non-linear filtering. The former one is fast and easy to implement. However, it produces a serious image blurring. Nonlinear filters can efficiently overcome this limitation and results in highly improved filtering performance but at the cost of high computational complexity. Few filtering algorithms have been proposed for performing image denoising and deblurring simultaneously. This paper presents a novel algorithm for the restoration of blur and noisy images for near real time applications. The proposed algorithm is based on PSF (Point Spread Function) estimation and Wiener filtering. The Wiener filter removes the additive noise and inverts the blurring simultaneously and thus performs an optimal trade-off between inverse filtering and noise suppressing. The Wiener filtering minimizes the overall mean square error in the process of noise suppressing. The PSF used for Wiener filtering is estimated using blind deconvolution. This is a noniterative process and provides faster results.