Determining the number of tetrominoe orders for denoising applications performed by Tetrolet transform

While Tetrolet transform (TT) which is one of the multi-resolution analysis improved recently is applied to images, 5 different shape called tetrominoes is gathered for 4×4 pixel blocks and TT is performed by arranging pixels according to this order. Tetrominoes can be chosen with 117 different combinations. In this study, firstly benchmark images (Lena, Barbara, Boat, Mandrill, Cameraman) and liver MR (magnetic resonance) images are denoised by utilizing TT with 1 to 117 different tetrominoe combinations respectively and optimal number of orders is determined for different noise rates by comparing obtained PSNR (peak signal to noise ratio) results. After that, images are denoised by using just optimal number of tetrominoe orders and all 117 combinations seperately. The operation times are compared. It is clearly seen that using all possible combinations for denoising causes redundant processes and an optimal number of tetrminoe orders can be specified for different image sets according to noise ratio. Decreasing number of combinations shortened the operation time seriously.