Seismic random noise suppression presents two main challenges: achieving thorough noise suppression while simultaneously ensuring complete restoration of effective signal content. However, due to the complexity of random noise, existing denoising methods often only achieve an awkward balance between removing random noise and restoring effective signals. In this paper, we propose a novel random noise suppression model based on downsampling and super-resolution. By decoupling the denoising and signal restoration processes, our method reduces the difficulty of addressing these two challenges and mitigates the likelihood of suboptimal results. On the one hand, the high fitting-capacity Downsampling network uses non-linear transformations to separate random noise and effective signals while purifying the high-order features of effective signals. On the other hand, the Super-resolution network expands the low-dimensional seismic signal content containing the high-order features of the signal to restore the signal structure. Moreover, we propose a new adversarial loss by introducing the gradient between the generated data and the real data, which enhances the perceptual quality of the super-resolution results and recovers the content of effective signals better. Because both subnetworks are not affected by signal/noise features during processing, the model exhibits strong fitting and generalization abilities. The experimental evaluation on four different types of seismic data demonstrates the superiority of our method in suppressing random noise and restoring the content of effective signals.

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.