Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization
01 Jan 2018-pp 115-144
TL;DR: This work has proposed a Termite Colony Optimization (TCO) algorithm based on the behavior of termites and uses the proposed algorithm and fuzzy entropy for satellite image contrast enhancement.
Abstract: Image enhancement is an essential subdomain of image processing which caters to the enhancement of visual information within an image. Researchers incorporate different bio-inspired methodologies which imitate the behavior of natural species for optimization-based enhancement techniques. Particle Swarm Optimization imitates the behavior of swarms to discover the finest possible solution in the search space. The peculiar nature of ants to accumulate information about the environment by depositing pheromones is adopted by another technique called Ant Colony Optimization. However, termites have both these characteristics common in them. In this work, the authors have proposed a Termite Colony Optimization (TCO) algorithm based on the behavior of termites. Thereafter they use the proposed algorithm and fuzzy entropy for satellite image contrast enhancement. This technique offers better contrast enhancement of images by utilizing a type-2 fuzzy system and TCO. Initially two sub-images from the input image, named lower and upper in the fuzzy domain, are determined by a type-2 fuzzy system. The S-shape membership function is used for fuzzification. Then an objective function such as fuzzy entropy is optimized in terms of TCO and the adaptive parameters are defined which are applied in the proposed enhancement technique. The performance of the proposed method is evaluated and compared with a number of optimization-based enhancement methods using several test images with several statistical metrics. Moreover, the execution time of TCO is evaluated to find its applicability in real time. Better experimental results over the conventional optimization based enhancement techniques demonstrate the superiority of our proposed methodology.
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Abstract: Micro-appearance models explicitly model the interaction of light with microgeometry at the fiber scale to produce realistic appearance. To effectively match them to real fabrics, we introduce a new appearance matching framework to determine their parameters. Given a micro-appearance model and photographs of the fabric under many different lighting conditions, we optimize for parameters that best match the photographs using a method based on calculating derivatives during rendering. This highly applicable framework, we believe, is a useful research tool because it simplifies development and testing of new models.Using the framework, we systematically compare several types of micro-appearance models. We acquired computed microtomography (micro CT) scans of several fabrics, photographed the fabrics under many viewing/illumination conditions, and matched several appearance models to this data. We compare a new fiber-based light scattering model to the previously used microflake model. We also compare representing cloth microgeometry using volumes derived directly from the micro CT data to using explicit fibers reconstructed from the volumes. From our comparisons, we make the following conclusions: (1) given a fiber-based scattering model, volume- and fiber-based microgeometry representations are capable of very similar quality, and (2) using a fiber-specific scattering model is crucial to good results as it achieves considerably higher accuracy than prior work.
530 citations
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457 citations
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391 citations
Posted Content•
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363 citations