In the present work, a novel signal denoising technique for piecewise constant or linear signals is presented termed as "signal split." The proposed method separates the sharp edges or transitions from the noise elements by splitting the signal into different parts. Unlike many noise removal techniques, the method works only in the nonorthogonal domain. The new method utilizes Stein unbiased risk estimate (SURE) to split the signal, Lipschitz exponents to identify noise elements, and a polynomial fitting approach for the sub signal reconstruction. At the final stage, merging of all parts yield in the fully denoised signal at a very low computational cost. Statistical results are quite promising and performs better than the conventional shrinkage methods in the case of different types of noise, i.e., speckle, Poisson, and white Gaussian noise. The method has been compared with the state of the art SURE-linear expansion of thresholds denoising technique as well and performs equally well. The method has been extended to the multisplitting approach to identify small edges which are difficult to identify due to the mutual influence of their adjacent strong edges.