Image enhancement based on contourlet transform
Images captured with insufficient illumination generally have dark shadows and low contrast. This problem seriously affects other forms of image processing schemes such as face detection, security surveillance, image fusion. In this paper, a new image enhancement algorithm using the important features of the contourlet transform is presented. A new transformation function is developed based on the existing sigmoid function and the tanh functions which have very interesting properties in enhancing images which are suffering from low illuminations or non-uniform lighting conditions. Literature dictates that contourlet transform has better performance in representing the image salient features such as edges, lines, curves, and contours than wavelets for its anisotropy and directionality and is therefore well suited for multiscale edge-based image enhancement. The algorithm works for gray scale and color images. For a color image, it is first converted from RGB (red, green, and blue) to HSI (hue, saturation, and intensity) color model. Then, the intensity component of the HSI color space is adjusted the preserving the original color using a new nonlinear transformation function. The simulation results show that this approach gives encouraging results for images taken in low-light and/or non-uniform lighting conditions. The results obtained are compared with other enhancement algorithms based on wavelet transform, curvelet transform, bandlet transform, histogram equalization (HE), and contrast limited adaptive histogram equalization. The performance of the enhancement based on the contourlet transform method is superior. The algorithm is checked for a total of 151 test images. A total of 120 of them are used for subjective evaluation and 31 are used for objective evaluation. For over 90% of the cases, the system is superior over the other enhancement methods.
image enhancement contourlet transform transformation function curvelet transform bandlet transform