This paper proposes a Poisson denoising method with a union of directional lapped orthogonal transforms (DirLOTs). DirLOTs are 2-D non-separable lapped orthogonal transforms with directional characteristics under the fixed-critically-subsampling, overlapping, orthonormal, symmetric, real-valued and compact-support property. In this work, Dir- LOTs are used to generate symmetric orthogonal discrete wavelet transforms and then a redundant dictionary as a union of unitary transforms. The multiple directional property is suitable for representing natural images which contain diagonal textures and edges. Multiple DirLOTs can overcome a disadvantage of separable wavelets in representing diagonal components. In addition to this feature, multiple DirLOTs make transformbased denoising performance better through the redundant representation. Experimental results show that the combination of the variance stabilizing transformation (VST), Steinïs unbiased risk estimator-linear expansion of threshold (SURE-LET) approach and multiple DirLOTs is able to significantly improve the denoising performance.