Traditionally, processing a compressed image requires decompression first. Following the related manipulations, the processed image is compressed again for storage. To reduce the computational complexity and processing time, manipulating images in the transform domain, which is possible, is an efficient solution; The uniform wavelet thresholding is one of the most widely used methods for image denoising in the Discrete Wavelet Transform (DWT) domain. This method, however, has the drawback of blurring the edges and the textures of an image after denoising. A new algorithm is proposed in this thesis for image denoising in the DWT domain with no blurring effect. This algorithm uses a suite of feature extraction and image segmentation techniques to construct filter masks for denoising. The novelty of the algorithm is that it directly extracts the edges and texture details of an image from the spatial information contained in the LL subband of DWT domain rather than detecting the edges across multiple scales. An added advantage of this method is the substantial reduction in computational complexity. Experimental results indicate that the new algorithm would yield higher quality images (both qualitatively and quantitatively) than the existing methods; In this thesis, new algorithm for image interpolation in the DWT domain is also discussed. Being different from other methods for interpolation, which focus on Haar wavelet, new interpolation algorithm also investigates other wavelets, such as Daubecuies and Bior. Experimental results indicate that the new algorithm is superior to the traditional methods by comparing the time complexity and quality of the processed image
