Abstract: In this paper, an edge-strength guided multiscale
retinex (EGMSR) approach will be proposed for color image contrast
enhancement. In EGMSR, the pixel-dependent weight associated with
each pixel in the single scale retinex output image is computed
according to the edge strength around this pixel in order to prevent
from over-enhancing the noises contained in the smooth dark/bright
regions. Further, by fusing together the enhanced results of EGMSR
and adaptive multiscale retinex (AMSR), we can get a natural fused
image having high contrast and proper tonal rendition. Experimental
results on several low-contrast images have shown that our proposed
approach can produce natural and appealing enhanced images.
Abstract: The approach based on the wavelet transform has
been widely used for image denoising due to its multi-resolution
nature, its ability to produce high levels of noise reduction and the
low level of distortion introduced. However, by removing noise, high
frequency components belonging to edges are also removed, which
leads to blurring the signal features. This paper proposes a new
method of image noise reduction based on local variance and edge
analysis. The analysis is performed by dividing an image into 32 x 32
pixel blocks, and transforming the data into wavelet domain. Fast
lifting wavelet spatial-frequency decomposition and reconstruction is
developed with the advantages of being computationally efficient and
boundary effects minimized. The adaptive thresholding by local
variance estimation and edge strength measurement can effectively
reduce image noise while preserve the features of the original image
corresponding to the boundaries of the objects. Experimental results
demonstrate that the method performs well for images contaminated
by natural and artificial noise, and is suitable to be adapted for
different class of images and type of noises. The proposed algorithm
provides a potential solution with parallel computation for real time
or embedded system application.