These methods have demonstrated its potential to overcome the gamma distortion without calibration.
Moreover, subspace clustering methods have demonstrated good efficiency on embedded systems.
Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods.
Wavelet based data compression methods have demonstrated superior performance over the conventional interpolative methods.
Specially, evolutionary image registration methods have demonstrated their ability as robust approaches to the problem.
The existing image super-resolution methods have demonstrated that making full use of image prior can improve the algorithm performance.
Both methods have demonstrated a notably good performance in several test cases with different patterns of the thermal demands.
These electroanalytical methods have demonstrated their ability and reliability to study fundamental science and provide practical applications.
Traditional non-deep-learning-based SR methods have demonstrated that image prior, e.g., local smoothing, nonlocal self-similarity, and sparsity, plays an important role in image SR.
Besides these, bio-affinity chromatographic methods have demonstrated to be valuable tools offering the advantageous characteristics such as simplicity, efficiency, high-throughput capability and robustness.
