Blind source separation using modified contrast function in fastICA algorithm
Alka Mahajan,Gajanan K. Birajdar +1 more
TL;DR: Simulation results show that the proposed nonlinear function used to separate image mixtures, results in faster execution and good quality image separation.
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Abstract: A novel contrast function is proposed to be used in fastICA algorithm for Blind Source Separation (BSS). Simulation results show that the proposed nonlinear function used to separate image mixtures, results in faster execution and good quality image separation. Peak Signal to Noise Ratio (PSNR), Improved Signal to Noise Ratio (ISNR), Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) are used to evaluate quality of separated images and Amari error is calculated to prove the performance of separation quality.
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Citations
Harmonic Separation Based on Independent Component Analysis Method
Yongle Ai,Haiyang Zhang +1 more
TL;DR: The result validates that harmonics can be separated effectively with a high precision by using the method proposed under the condition with low real-time demand.
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Modified convex divergence ICA for separation of mixed images
D. Sugumar,P. T. Vanathi,A. Mary Jasmine +2 more
- 01 Feb 2014
TL;DR: The aim of this paper is to achieve faster and accurate separation which is accomplished by the modified CDIV-ICA algorithm, which converges faster in finding the demixing vector to separate the components compared to other methods.
1
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Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: A novel fast algorithm for independent component analysis is introduced, which can be used for blind source separation and feature extraction, and the convergence speed is shown to be cubic.
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