Comparison of Different Image Enhancement Algorithms
Keywords:
Image Enhancement, Contrast Stretching, Histogram Equalizations, IMF, Sharpening image. Empirical Mode DecompositionAbstract
This project proposes different enhancement algorithms for blur images. It is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. An Empirical Mode Decomposition (EMD) based blur image enhancement algorithm is presented for this purpose. EMD is a signal decomposition technique which is particularly suitable for the analysis of non-stationery and non-linear data. An Empirical Mode Decomposition (EMD) based blur image enhancement algorithm is presented for this purpose. EMD is a signal decomposition technique which is particularly suitable for the analysis of non-stationery and non-linear data. In EMD, initially each spectral component of an blur image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. The lower order IMFs capture fast oscillation modes (high spatial frequencies in images) while higher order IMFs typically represent slow spatial oscillation modes (low spatial frequencies in images).Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image.
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