Hybrid Approach for Denoising of Degraded Images

Denoising of images using hybrid approach has been a predominant scenario in the current trends. The hybrid approach refers in giving up a combined framework to utilize the advantage of both Fourier and wavelet domain schemes. The noisy images including their Region of Interest (ROI) can yield encouraging results, when obtained through the proposed hybrid approach when comparing to the single transform domain method. Thus the motivation for the hybrid method has been the realization that shrinkage in single transform cannot yield good estimates in deconvolution problems. The main focus of this paper is denoising of degraded images in which the blurring operator inversion is followed by noise attenuation through scalar shrinkage in both the Fourier and wavelet domain. The Fourier shrinkage utilizes the structure of the noise inherent in deconvolution. While the wavelet shrinkage utilizes the piecewise smooth structure of real-world signals and images. The advantage of denoising in Fourier domain is the ability to deal with overlapping responses. But in some exceptions when Fourier domain is alone used, noise amplification might be a disadvantage. Noise can be reduced in the frequency domain by shrinking the frequency coefficients, but noise and signal may be difficult to separate. Wavelet transforms have advantages over traditional Fourier transforms for representing functions that have discontinuities and sharp peaks, and for accurately reconstructing finite, non-stationary signals. Hence in this paper it is solved by using the combined Fourier and Wavelet shrinkage. Keywords—Deconvolution, Fourier domain, Wavelets, Restoration, Smoothness, Estimation.