Abstract: To solve the problems of the existing depth acquisition methods with missing data and low resolution, the novel depth enhancement method based on soft clustering was proposed and named the soft clustering solver. By the strong edge-preservation of the soft clustering method, the accuracy of depth enhancement could be improved. The affinity matrix derived from the soft clustering was combined with the weighted least square model to establish the confidence-weighted least square model in the solver, and the iteratively based solution method was proposed. To evaluate the proposed method, the experiments on several depth enhancement tasks of depth inpainting, depth super-resolution and depth rectification were conducted. Various evaluation metrics were used, including peak signal to noise ratio（PSNR）, structural similarity index measure（SSIM）, rooted mean squared error（RMSE） and running time. The results show that for depth inpainting, the average PSNR reaches 42.28 with average SSIM of 98.83%. For depth super-resolution and depth rectification, the average RMSE values are 8.96 and 2.36, respectively. By the proposed method, the image with resolution of 2 048×1 024 pixels can be processed with only 5.03 s.