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Image Denoising Base on SIFT and Chaotic Hopfield Neural Network Swarm Optimization | ||
| Iraqi Journal of Information Technology | ||
| Article 1, Volume 7, Issue 4, 2017, Pages 89-105 | ||
| Author | ||
| Shymaa Mohammed Jameel | ||
| Abstract | ||
| Many techniques and filters were used in the image noise removal for different types of noises distributions and locations. The intelligent filters utilized the denoising functionality with a best accuracy and speed operation. In this paper, the technique suggest to image denoising uses the SIFT algorithm (Scale-invariants features transform) for detecting and describe local features in images and chaotic Hopfield neural network swarm optimization in order to detect and remove the some unwanted details and noise without blurring the denoised image. The SIFT algorithm was used to detect the local features of the important and references image features to help the chaotic neural network to avoid the wanted features without changing. Also to increase the chaotic neural network accuracy while the chaotic function used to develop the Hopfield neural network to avoid the local minima and weights optimization. An acceptable PSNR and MSE results comparing with others with a good image visions results. | ||
| Keywords | ||
| Image denoising; Hopfield neural; chaotic neural; chaotic Hopfield neural; CHNN; SIFT | ||
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