Guided trilateral filter and its application to ultrasound image despeckling

Abstract Speckle reduction has been a hot research topic in ultrasound imaging community. However, designing a method with better despeckling performance and lower algorithm complexity is still an active pursuit. In this paper, we propose a novel local spatial filtering framework named as the guided trilateral filter (GTF) to implement restoration of a noisy image with a well-fitting statistical distribution model. The GTF is derived as the local maximum likelihood estimation from the probabilities of the residuals between the filtered/guided image and the noisy image. The resulting iteration algorithm is essentially a local weighted filtering whose weights come from the guided image, including spatial distance, range discrepancy and statistical distribution. Choosing specific functions for these trilateral weights can direct to some classical local spatial filters, such as classical bilateral filter, robust bilateral filter, joint/cross bilateral filter, speckle reduction bilateral filter, etc. As a despeckling application of the GTF, we embed the Fisher-Tippett distribution model of ultrasound image into the GTF and thereby present the guided trilateral despeckling filter (GTDF). Experimental results on synthetic and real ultrasound images have demonstrated that the GTDF not only can achieve superior performance against state of the art methods, but also has fast and robust convergence and parameter setting insensitivity.