Adaptive thresholding wavelet based denoising using whale optimization algorithm

Generally images have poor contrast along with serious types of noises. The suppression of noise in medical images corrupted by Gaussian white noise is a major issue in diverse image processing and computer vision problems. Image denoising systems are important for accurate clinical diagnosis. The purpose of this study is to present a simple and effective iterative multistep image denoising system based on adaptive wavelet transform (AWT) using whale optimization algorithm where the denoised image from one stage is the input to the next stage. The denoising process stops when a particular condition measured by image energy is adaptively achieved. The proposed scheme is tested on images and performance is measured by the well-known peak-signal-to-noise-ratio (PSNR) and SSIM statistic. Proposed algorithm has been validated through ultrasound image corrupted by a variety of noise densities through Gaussian noise. Simulation results show that the proposed method outperforms the existing denoising methods.