Image denoising based on a convolution neural network (CNN) can be described as the problem of learning a mapping function from a noisy image to a clean image through an end-to-end training. We propose a multiscale parallel feature extraction module (MPFE) for CNN denoising, which integrates residual learning and dense connection. The MPFE uses convolution kernels of different sizes to adaptively extract multiple features in different scales from the input image. We introduce dense connection to connect each MPFE, which can make different features interact with each other and concatenate together, so as to fully exploit the image features. The dense connection can pass the features that carry many image details, which help reduce image distortion. Meanwhile, it can also reduce gradient disappearance and improve convergence speed. The MPFE uses residual learning to resolve the gradient loss caused by high network depth while still ensuring that the network learns the details of the noisy image. Simulation experiments show that our denoising method has the ability of suppressing Gaussian noises with different noise levels, it performs superior performance over the state-of-the-art denoising methods.
Edge detection is one of the most commonly used operations in image analysis of license plate. The classical edge detection algorithm based on wavelet transform utilizes a threshold to remove noise from license plate image, and then detects edge with wavelet transform. In the condition with strong noise, the classical edge detection algorithm often works not well enough. The proposed edge detection algorithm combines the wavelet transform and quantum genetic algorithm and significantly improves the result. Specifically, wavelet decomposition is applied to get a set of low and high frequency sub-images from image of license plate and quantum genetic algorithm is applied to optimize wavelet denoising threshold which removing the noise of the high-frequency sub-images, finally the edge image is obtained by reconstructing the high-frequency sub-images without noise. The efficiency of the method is better than the classical one and is proved by computer simulation.
KEYWORDS: Mirrors, Genetic algorithms, Control systems, Genetics, Detection and tracking algorithms, Sensors, MATLAB, Control systems design, Computing systems, Binary data
A brief introduction of an optic-electronic tracking system using a fast reflection mirror is presented, the design of the fast reflection mirror control system, based on immune genetic PID algorithm, is described. Besides the ability of a stochastic global searching of simple genetic algorithm, the immune genetic algorithm used involves mechanisms which exist in biological immune systems, such as antigen memory, antibody encouragement and restraint, antibody diversity keeping, etc. By combining an algorithm of biological immunity with a genetic algorithm, a kind of optimal computing model is proposed based on a genetic mechanism of immunity. Taking immune genetic algorithm as a base, the optimal parameters of a PID controller are obtained. The simulation results of an immune genetic PID algorithm for the tracking system using a fast reflection mirror are presented, which show that the fast reflection mirror control system based on an immune genetic PID algorithm possesses superior qualities, indicating good prospects of application.
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