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The parameter evaluation of reservoir rocks can help us to identify components and calculate the permeability and other parameters, and it plays an important role in the petroleum industry. Until now, computed tomography (CT) has remained an irreplaceable way to acquire the microstructure of reservoir rocks. During the evaluation and analysis, large samples and high-resolution images are required in order to obtain accurate results. Owing to the inherent limitations of CT, however, a large field of view results in low-resolution images, and high-resolution images entail a smaller field of view. Our method is a promising solution to these data collection limitations. In this study, a framework for sparse representation-based 3D volumetric super-resolution is proposed to enhance the resolution of 3D voxel images of reservoirs scanned with CT. A single reservoir structure and its downgraded model are divided into a large number of 3D cubes of voxel pairs and these cube pairs are used to calculate two overcomplete dictionaries and the sparse-representation coefficients in order to estimate the high frequency component. Future more, to better result, a new feature extract method with combine BM4D together with Laplacian filter are introduced. In addition, we conducted a visual evaluation of the method, and used the PSNR and FSIM to evaluate it qualitatively.

We address space-time super-resolution (SR) problem in this paper. To efficiently explore the correlations within video sequences, a patch group (PG) model is proposed. The model is based on a novel 3D neighborhood system (NS) and embeds the spatial and temporal correlations. A patch group cuts (PGCuts) metric is then built on the model to provide a new prior for space-time SR reconstruction. To balance the prior strength for the whole video sequence, an adaptive scheme is also considered. We evaluate the proposed PGCuts prior on both synthesized and real video sequences. The results indicate that space-time SR method with the PGCuts prior outperforms other ones by retaining smooth edges and motion trajectories in videos, and by staying robust to noises as well. 3D neighborhood system accommodates more pixel correlations than traditional one.Patch group model based on similarity efficiently exploits space-time coherence.Smooth surfaces in patch group model results in smooth edges and motion trajectories.Edge strength based adaptive scheme effectively balances performance in the whole video.SR with patch group cuts produces better objective and subjective performance.