Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to monitor ablated regions in human patients after minimally invasive microwave ablation procedures. Displacement estimation for EDE is commonly plagued by decorrelation noise artifacts degrading displacement estimates. In this paper, we propose a global dictionary learning approach applied to denoising displacement estimates with an adaptively learned dictionary from EDE phantom displacement maps. The resulting algorithm is one that represents displacement patches sparsely if they contain low noise and averages remaining patches thereby denoising displacement maps while retaining important edge information. The results of dictionary-represented displacements presented with a higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) with improved contrast, as well as improved phantom inclusion delineation when compared to initial displacements, median-filtered displacements, and spline smoothened displacements, respectively. In addition to visualized noise reduction, dictionary-represented displacements presented with the highest SNR, CNR, and improved contrast with values of 1.77, 4.56, and 4.35 dB, respectively, when compared to axial strain tensor images estimated using the initial displacements. Following EDE phantom imaging, we utilized dictionary representations from in vivo patient data, further validating efficacy. Denoising displacement estimates are a newer application for dictionary learning producing strong ablated region delineation with little degradation from denoising.