A new paper is added to the collection of reproducible documents:
Random noise attenuation using local signal-and-noise orthogonalization
We propose a novel approach to attenuate random noise based on local signal-and-noise orthogonalization. In this approach, we first remove noise using one of the conventional denoising operators, and then apply a weighting operator to the initially denoised section in order to predict the signal-leakage energy and retrieve it from the initial noise section. The weighting operator is obtained by solving a least-squares minimization problem via shaping regularization with a smoothness constraint. Next, the initially denoised section and the retrieved signal are combined to form the final denoised section. The proposed denoising approach corresponds to orthogonalizing the initially denoised signal and noise in a local manner. We evaluate denoising performance by using local similarity. In order to test the orthogonalization property of the estimated signal and noise, we calculate the local similarity map between the denoised signal section and removed noise section. Low values of local similarity indicate a good orthogonalization and thus a good denoising performance. Synthetic and field data examples demonstrate the effectiveness of the proposed approach in applications to noise attenuation for both conventional and simultaneous-source seismic data.