1) There are 6 noise models in image restoration. List ALL of them 2) Noise is often annoyance but can helps to improve fine details bridging gaps. Explain how this can be achieve 3) Contraharmonic is useful for impulse noise but required classification of the positive and negative sign. Elaborate on this. 4) Mean filter resulted in blurry and smooth image. Explain the consequences of mean filter compared to order statistic filter, 5) Where h(x,y) is the spatial representation of the degradation function, Write the model in an equivalent frequency domain representation 6) In medical imaging, noise presented with the images during acquisition. State ONE solution that is necessary before images are send for reporting. 7) Periodic noise can be reduce significantly in frequency domain. Explain your answer if using the local thresholding to remove the periodic noise

DIGITAL IMAGE PROCESSING

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