7,290 research outputs found
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
Exact Histogram Specification Optimized for Structural Similarity
An exact histogram specification (EHS) method modifies its input image to
have a specified histogram. Applications of EHS include image (contrast)
enhancement (e.g., by histogram equalization) and histogram watermarking.
Performing EHS on an image, however, reduces its visual quality. Starting from
the output of a generic EHS method, we maximize the structural similarity index
(SSIM) between the original image (before EHS) and the result of EHS
iteratively. Essential in this process is the computationally simple and
accurate formula we derive for SSIM gradient. As it is based on gradient
ascent, the proposed EHS always converges. Experimental results confirm that
while obtaining the histogram exactly as specified, the proposed method
invariably outperforms the existing methods in terms of visual quality of the
result. The computational complexity of the proposed method is shown to be of
the same order as that of the existing methods.
Index terms: histogram modification, histogram equalization, optimization for
perceptual visual quality, structural similarity gradient ascent, histogram
watermarking, contrast enhancement
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