An improvement of unsupervised design method for weighted median filters using GA

An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

“…( , ) is the weight; a pixel is more important if it is closer to the noise point. Therefore, [ [22][23][24][25][26]. The performance of the proposed detector could be seen in Table 1, which shows data on the images taken of Lena, baboon, peppers, cameraman, and Barbara as test images, with 10% to 90% of noise density added.…”

“…The process of training neural networks by the implementation of a gradient-based optimization algorithm (e.g., backpropagation) will lead to locally optimal solutions which may be far removed from the global optimum. However, evolutionary optimization methods offer a procedure to stochastically search for suitable weights and bias terms given a specific network topology [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In consequence, so as to solve our problem, we can adopt GA-BPN as a noise detector.…”

“…By comparison, we adapted a three-layer neural network, while the number of input layer and output layer was determined by the original image samples. So our main work is to optimize hidden-layer nodes number [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. These steps can be expressed as follows.…”

A principal palm-line extraction method for palmprint images based on diversity and contrast

“…Under some conditions, median filtering [12] can eliminate the impulse noise effectively and keep good edge feature. A median filtering function ) , ( j i I is defined as:…”