In the field of optical coherence tomography, reducing the speckle noise while protecting the textural features of image edge is difficult mainly because of the speckle residue and textural blur of edge in the speckle denoising process. To solve this problem, this study proposes a shearlet-transform-based improved total variation speckle denoising method. By combining the shearlet transform with the traditional total variation model, as well as a targeted denoising strategy applied on different image regions, the proposed method reduces the speckle noise without disturbing the texture in the image, and further improves the speckle-noise suppression in the original optical coherence tomography image. The proposed method is tested on many retinal optical coherence tomography images under different physiological and pathological conditions. Results show that the regional targeted strategy in the proposed method improves the ability of speckle-noise suppression, while the shearlet transform improves the ability of the edge texture protection, resulting in simultaneous speckle reduction and texture protection. The effectiveness of the proposed method is also confirmed in comparison with other common speckle denoising methods.