Abstract
The implementation of the regularized inverse filter involves the estimation of the power spectrum of the original image in the spatial domain. Since wavelet transforms have good decor relation property, the wavelet coefficients of the image can be better modeled in a stochastic model, and the power spectrum can be better estimated. Registration algorithms compute transformations to set correspondence between the two images the purpose of this paper is to provide a comprehensive review of the existing literature available on Image registration methods. We believe that it will be a useful document for researchers longing to implement alternative Image registration methods for specific applications.
Highlights
The Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering amplify the noise
Image registration finds its applications in various fields like remote sensing, environmental monitoring, change detection, image mosaicing, weather forecasting, creating super-resolution images, integrating information into geographic information systems (GIS)), in medicine (combining data from different modalities e.g. computer tomography (CT) and magnetic resonance imaging (MRI), to obtain more complete information about the patient, monitoring tumor growth (Figure 1)
There is no general theory for determining whatgood’ image enhancement is when it comes to human perception. It is good! when image enhancement techniques are used as pre-processing tools for other image processing techniques, quantitative measures can determine which techniques are most appropriate
Summary
The Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering amplify the noise. This suggests that a denoising step is needed to remove the amplified noise. Wavelet-based denoising scheme, a successful approach introduced recently by Donoho, provides a natural technique for this purpose. The image restoration contains two separate steps: Fourierdomain inverse filtering and wavelet-domain image denoising. Donoho's approach for image restoration improves the performance; in the case when the blurring function is not invertible, the algorithm is not applicable. The idea is simple: employ both Fourier-domain Wiener-like and wavelet-domain regularization
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