Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A hybrid evolutionary learning algorithm for TSK-type fuzzy model design
Mathematical and Computer Modelling: An International Journal
A new algorithm for exposure control based on fuzzy logic for video cameras
IEEE Transactions on Consumer Electronics
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this study, we proposed a new technique to compensate the backlight images. Two processing stages, called the backlight level detection and the backlight image compensation, are proposed. In the backlight level detection stage, we first transferred the color space to gray space by feature weighting, then obtain two backlight factors. We apply these two backlight factors to the proposed functional-link-based neuro-fuzzy network (FNFN) with immune particle swarm optimization (IPSO) for detecting compensation degree. In the backlight image compensation stage, we also proposed the adaptive cubic curve method to compensate and enhance the brightness of backlight images according to the compensation degree of each image. The backlight degree is indicated by histograms of the luminance distribution in the backlight level detection stage. The experiment results showed that the backlight images can be compensated effectively.