For the US visualisation based on denoising and enhancement, it is relevant to address speckle noise, which is a multiplicative noise generated when the scale of organ tissue’s structures is close to the ultrasonic wavelengths; different waves are reflected and overlap, causing the perturbation in the signals and appearing in the output image as darker and brighter grains. Ultrasonic image denoising has pros and cons: noise reduction improves visual evaluation, and the application of post-processing methods that use local information and are therefore influenced by noisy elements that alter the accuracy of numerical operators, such as convolution and interpolation. We discuss the main properties of ultrasound image denoising (Sect. 3.1). Then, we review state-of-the-art denoising methods classified according to their class: non-local (Sect. 3.2), anisotropic (Sect. 3.3), spectral (Sect. 3.4), learning-based (Sect. 3.5), and low-rank (Sect. 3.6). Finally, we present a comparison of the presented methods in terms of denoising results and execution time (Sect. 3.7).