Random Noise Redunction of Seismic Data in NSCT Domain The suppression of random noise in seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) of seismic data is much lower which bring great difficulty to seismic data processing and interpretation. In this paper, we propose a new seismic random noise suppression approach based on the nonsubsampled contourlet transform (NSCT). Firstly, the noisy seismic data is decomposed into different subbands of frequency and orientation responses using NSCT. Then, we use an appropriate thresholding operator to denoise the subbands of NSCT coefficients. Finally, we perform the inverse NSCT transform on the denoised NSCT components to reconstruct the denoised seismic data. We use synthetic data examples to demonstrate the superior performance of the proposed approach over the wavelet transform based threshold denoising method.

A Fast Iterative Method for Removing Sparse Noise from Sparse Signals Reconstructing a signal corrupted by impulsive noise is of high importance in several applications, including impulsive noise removal from images, audios and videos, and separating texts from images. Investigating this problem, in this paper we propose a new method to reconstruct a noisecorrupted signal where both signal and noise are sparse but in different domains. We apply our algorithm for impulsive noise (Salt andPepper Noise (SPN) and RandomValued Impulsive Noise (RVIN)) removal from images and compare our results with other notable algorithms in the literature. Simulation indicates show that our algorithm is not only simple and fast, but also it outperforms the other stateoftheart methods in terms of reconstruction quality andor complexity.