Low contrast structure can be found in many kinds of digital images

and it is a meaningful work to find out how to enhance these raw images through digital post-processing. A novel enhancement algorithm based on multi-scale edges representation of images is proposed. This algorithm is motivated by the connection between the contrast of a grayscale image and the gradient magnitude of intensity edges in the neighborhood where the contrast is measured. The undecimated dyadic wavelet transform of the original image is computed firstly by treating the columns and lines of the image separately

and then the local maxima of wavelet transform coefficients are selected out. The reconstruction of the image can be interpreted as an interpolation process

which recovers the wavelet coefficients between two consecutive modulus maxima and then calculates the inverse wavelet transform. As we know

the first derivatives of the modulus maxima are zero

and the Hermite polynomial requires the value of derivatives at the given nodes. Based on these two facts

the wavelet coefficients can be reconstructed using Hermite interpolation polynomial of degree 3 between any two adjacent maxima. By means of stretching those maxima at different levels and interpolating them with Hermite interpolation polynomials

the image can be enhanced effectively

and the different stretching factors on different levels can provide various kinds of enhancing effects

while this kind of enhancing flexibility cannot be found in other algorithms easily. This algorithm also offers abilities to control noise magnification and to enhance features of certain size within the images. Numerical experiments show that

the method can get fairly well enhancement result and the computing complexity can be low

so it is a practical fast enhancement algorithm.