Aiming at the problem of the traditional neural network for non-uniformity correction easy to cause ghosting artifacts and image blurring, an improved non-uniformity correction algorithm based on neural network is proposed. Firstly, a new fast trilateral filter is designed, which can be regarded as an edge-preserving smoothing operator. Secondly, in order to stabilize and accelerate the learning process, it adopts the self-adaptive learning rate and applies additional momentum factor to the neural network. Thirdly, in order to update the calibration parameters accurately, the local motion of different areas is judged carefully. The simulating experiments indicate that the proposed algorithm can suppress the ghosting artifacts and the image degradation. And it has better performance compared with other algorithms.
Curvature filter and gradient transform based image enhancement algorithm can effectively suppress noises and enhance image edges. However, it is very hard to be carried out in real time due to the large computing load. To address this problem, a GPU based parallel implementation is proposed in this paper. First, aiming at the characteristics of the algorithm, a numerical implementation method based on central-difference is proposed. Then a domain decomposition scheme is utilized in parallel Gaussian curvature filter to remove the dependence of neighboring pixels and guarantee convergence. Finally, we make the multiprocessor wrap occupancy reach 100% by optimizing the thread grid and register usage. Experimental results demonstrate that our parallel method runs 200-300 times faster than CPU serial method with real time processing of 4096×4096 resolution image, which indicates a great potential for application.
To reduce the influence of noise in infrared spectral signal measurement, a topological derivative improved partial differential equation method for infrared spectral data denoising is proposed. As an indicator function, topological derivative through a minimization process to find the best position to introduce disturbance, where are spectral edge points, then select the most excellent diffusion coefficient, so the cost of the minimum functional value. Based on the idea of topological optimization, it makes the lowest topological derivative to be optimum one. Then the diffusion is applied by using partial differential equation. Several simulated infrared spectral sequences are utilized to verify the performance of the proposed method. The experiment results show that our method is better in denoising.
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