Performance Analysis of Real and Complex Wavelet Transform Techniques used for Wavelet-Based Image Denoising

Image de noising is a principal technique majorly used for original image restoration, segmentation and image classification. It is basically used to refine the images by eliminating noise embedded. In the current work, authors present a denoising technique based on Wavelet Domain Filtering. Denoising of images after domain transform helps in separating the noise and data components. The discrete wavelet transform and dual tree complex wavelet transforms work on the analysis and synthesis filter banks to filter and further segment the noisy input signal to low frequency and high frequency components constituting data artifacts and noise respectively. The progressive decomposition of data to a particular number of levels finally results in a noise-free output after filtering, considering a particular threshold. A comparative analysis of thresholding techniques is presented and evaluated for the parameters Signal to Noise Ratio (SNR) and lowest Root Mean Square Error Value (RMSE). The simulation results indicate superior performance of dual tree complex wavelet transform(DTCWT) when compared to the discrete wavelet transform.