Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

Underwater images regularly exhibit color deviation and low contrast attributed to attenuation and scattering associated with wavelength and distance. For solving these two degradation problems, we propose an efficient underwater image enhancement model which takes a multi-scale fusion strategy as a pivot to concurrently achieve color balance and contrast enhancement. Specifically, we target two core degradation problems through two pre-processing steps. On the one hand, the white balance strategy enhances the appearance of the image by suppressing unnecessary colors to solve the color deviation problem. On the other hand, we employ a color-preserving adaptive histogram equalization (CP-AHE) to obtain an image with higher contrast by jointly processing the three color channels. In the fusion phase, a fusion strategy based on the detail-preserving decomposition of the edge-protected structural blocks is used to converge the white balance and CP-AHE inputs. Extensive experiments on common underwater image datasets have shown the advantages of the proposed method over other state-of-the-art underwater image methods in terms of visual and quantitative evaluation.

Image denoising is a common problem during image processing. Salt and pepper noise may contaminate an image by randomly converting some pixel values into 255 or 0. The traditional image denoising algorithm is based on filter design or interpolation algorithm. There exists no work using the convolutional neural network (CNN) to directly remove salt and pepper noise to the authors’ knowledge. In this study, they utilise CNN with the multi‐layer structure for the removal of salt and pepper noise, which contains padding, batch normalisation and rectified linear unit. In training, they divide images into three parts: training set, validation set and test set. Experimental results demonstrate that the architecture can effectively remove salt and pepper noise for the various noisy images. In addition, their model can remove high‐density noise well due to the extensive local receptive fields of the deep neural networks. Finally, extensive experimental results show that their denoiser is effective for those images with a large number of interference pixels which may cause misjudgement. In a word, they generalise the application of CNN to salt and pepper noise removal and obtain competitive results.

Physics-informed neural networks (PINNs) improve the accuracy and generalization ability of prediction by introducing physical constraints in the training process. As a model combining physical laws and deep learning, it has attracted wide attention. However, the training cost of PINNs is high, especially for the simulation of more complex two-phase Darcy flow. In this study, a physics-informed radial basis function neural network (PIRBFNN) is proposed to simulate two-phase Darcy flow of oil and water efficiently. Specifically, in each time step, oil phase and water phase equations are discretized based on the finite volume method, and then, the physics-informed loss is constructed according to the residual of their coupling equations, and the pressure is approximated by RBFNN. Based on the obtained pressure, another physics-informed loss is constructed according to the residual of discrete water phase equation and the water saturation is approximated by another RBFNN. For boundary conditions, we use “hard constraints” to speed up the training of PIRBFNN. The straightforward structure of PIRBFNN also contributes to an efficient training process. In addition, we have simply proved the ability of RBFNN to fit continuous functions. Finally, the experimental results also verify the computational efficiency of PIRBFNN. Compared with physics-informed convolutional neural network, the training time of PIRBFNN is reduced by more than three times.