Image Noise Suppression is a highly demanded approach in digital imaging systems design. Impulsive noise is one such noise, which is frequently encountered problem in acquistion, transmission and processing of images. In the area of image restoration, many state-of-the art filters consist of two main processes, classification (detection) and reconstruction (filtering). Classification is used to separate uncorrupted pixels from corrupted pixels. Reconstruction involves replacing the corrupted pixels by certain approximation technique. In this thesis such schemes of impulsive noise detection and filtering thereof are proposed. Impulsive noise can be Salt & Pepper Noise (SPN) or Random Valued Impulsive Noise (RVIN). Only RVIN model is considered in this thesis because of its realistic presence. In the RVIN model a corrupted pixel can take any value in the valid range. Adaptive threshold selection is emphasized for all the four proposed noise detection schemes. Incorporation of adaptive threshold into the noise detection process led to more reliable and more efficient detection of noise. Based on the noisy image characteristics and their statistics, threshold values are selected. To validate the efficacy of proposed noise filtering schemes, an application to image sharpening has been investigated under the noise conditions. It has been observed, if the noisy image passes through the sharpening scheme, the noise gets amplified and as a result the restored results are distorted. However, the prefiltering operations using the proposed schemes enhances the result to a greater extent. Extensive simulations and comparisons are done with competent schemes. It is observed, in general, that the proposed schemes are better in suppressing impulsive noise at different noise ratios than their counterparts.