PERFORMANCE ANALYSIS OF IMAGE DENOISING IN LIFTING WAVELET TRANSFORM

Images are contaminated by noise due to several unavoidable reasons, Poor image sensors, imperfect instruments, problems with data acquisition process, transmission errors and interfering natural phenomena are its main sources. Therefore, it is necessary to detect and remove noises present in the images. Reserving the details of an image and removing the random noise as far as possible is the goal of image denoising approaches . Lifting wavelet transform (LWT)is based on the theory of lazy wavelet and completely recoverable filter banks, improving the wavelet and its performance through the lifting process under the condition of maintaining the feature of the wavelet compared with the classical constructions (DWT) is rely on the Fourier transform. In this paper we compare the image denoising performance of LWT with DWT . We demonstrated through simulations with images contaminated by white Gaussian noise that exhibits performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect.