Morphological Contrast Enhancement of Gray Scale and Color Images Based on Weber’s Law
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Abstract
Weber‟s law suggests a logarithmic relationship between perceptual stimuli and human perception. The Weber sampler is an adap tive,
non uniform sampling mechanism that exploits Weber‟s law to sample the signal at a minimum rate without significant perceptual degradation.
In this paper, We apply this fact to design contrast enhancement method for various images that improves local image contrast by controlling the
local image gradient. We pose the contrast enhancement as an optimization problem that maximizes the average local contrast of an image
strictly constrained by perceptual constraint derived from the Weber law.
In this paper, we introduce Morphological transformation and Block analysis is used to detect the background of various gray level and color
images. These techniques are first implemented in gray scale and then extended to color images by individually enhancing the color components.
Some morphological transformations are used to detect the background in color images characterized by poor contrast. Contrast operators are
based on the logarithm function in a similar way to Weber‟s law. The use of the logarithm function avoids abrupt changes in lighting. The results
of each technique are illustrated for various backgrounds, majority of them in poor lighting condition. The complete image processing is done
using JAVA simulation model.
Keywords: Morphological transformation ,Weber‟s law, contrast enhancement, block analysis, opening by reconstruction.
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