Understanding retinal color coding from first principles
Neural Computation
What is the goal of sensory coding?
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
How Close Are We to Understanding V1?
Neural Computation
Learning Overcomplete Representations
Neural Computation
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A fast and simple solution was suggested to reduce the inter-pixels correlations in natural images, of which the power spectra roughly fell off with the increasing spatial frequency f according to a power law; but the 1/f exponent, α, was different from image to image. The essential of the proposed method was to flatten the decreasing power spectrum of each image by using an adaptive low-pass and whitening filter. The act of low-pass filtering was just to reduce the effects of noise usually took place in the high frequencies. The act of whitening filtering was a special processing, which was to attenuate the low frequencies and boost the high frequencies so as to yield a roughly flat power spectrum across all spatial frequencies. The suggested method was computationally more economical than the geometric covariance matrix based PCA method. Meanwhile, the performance degradations accompanied with the computational economy improvement were fairly insignificant.