How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution

Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute sparsely in the spatial space. These inspire us to propose a low-rank solution which effectively integrates two learning methods and then achieves a superior result. To fit this solution, the internal learning method and the external learning method are tailored to produce multiple preliminary results. Our theoretical analysis and experiment prove that the proposed low-rank solution does not require massive inputs to guarantee the performance, and thereby simplifying the design of two learning methods for the solution. Intensive experiments show the proposed solution improves the single learning method in both qualitative and quantitative assessments. Surprisingly, it shows more superior capability on noisy images and outperforms state-of-the-art methods

Numerical analysis of heat transfer in pulsating turbulent flow in a pipe

Convection heat transfer in pulsating turbulent flow with large velocity oscillating amplitudes in a pipe at constant wall temperature is numerically studied. A low-Reynolds-number (LRN) k–ε turbulent model is used in the turbulence modeling. The model analysis indicates that Womersley number is a very important parameter in the study of pulsating flow and heat transfer. Flow and heat transfer in a wide range of process parameters are investigated to reveal the velocity and temperature characteristics of the flow. The numerical calculation results show that in a pulsating turbulent flow there is an optimum Womersley number at which heat transfer is maximally enhanced. Both larger amplitude of velocity oscillation and flow reversal in the pulsating turbulent flow also greatly promote the heat transfer enhancement

Numerical analysis of heat transfer in pulsating turbulent flow in a pipe

Convection heat transfer in pulsating turbulent flow with large velocity oscillating amplitudes in a pipe at constant wall temperature is numerically studied. A low-Reynolds-number (LRN) k–ε turbulent model is used in the turbulence modeling. The model analysis indicates that Womersley number is a very important parameter in the study of pulsating flow and heat transfer. Flow and heat transfer in a wide range of process parameters are investigated to reveal the velocity and temperature characteristics of the flow. The numerical calculation results show that in a pulsating turbulent flow there is an optimum Womersley number at which heat transfer is maximally enhanced. Both larger amplitude of velocity oscillation and flow reversal in the pulsating turbulent flow also greatly promote the heat transfer enhancement