Genetic algorithms for optimal image enhancement

Pal, Sankar K.; Bhandari, Dinabandhu; Kundu, Malay K.

doi:10.1016/0167-8655(94)90058-2

Cited by 106 publications

(30 citation statements)

References 5 publications

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“…General approaches have also been put forth that consider low-level image processing as an optimization problem, with some of the early works focussing on basic image enhancement [49,56]. Such an approach is investigated here as a potential low-level transform forming part of the combined low-and mid-level image processing search landscape.…”

Section: Genetic Transformmentioning

confidence: 99%

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Abstract:Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid-and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization.

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Section: Genetic Transformmentioning

confidence: 99%

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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“…A user typically needs to digitize or provide examples of desired segmentation results. Such an approach has attracted research attention in the imaging disciplines in general [26,28,[31][32][33][34][35] and also more specifically in the context of remote sensing image analysis [27,29,36]. It is a feasible strategy if a scene contains numerous "similar" elements that are of interest, common in many mapping tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user OPEN ACCESS Remote Sens. 2014, 6 11853 provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

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“…Recently natures inspired population based metaheuristics have been devised to solve optimization problems [7]. So, they can also apply in image processing field where some problems like image enhancement, segmentation etc has been considered as an optimization problems [8,9,10,11]. Differential Evolution and Genetic Algorithm are stochastic and robust metaheuristics in the field of evolutionary computation and also used in image processing field to solve optimization problems [12,10,11,30].…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Lévy flight Approach in Bat and Firefly Algorithm for Gray level image Enhancement

Dhal¹,

Quraishi²,

Das³

2015

IJIGSP

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Abstract-Recently nature inspired metaheuristic algorithms have been applied in image enhancement field to enhance the low contrast images in a control manner. Bat algorithm (BA) and Firefly algorithm (FA) is one of the most powerful metaheuristic algorithms. In this paper these two algorithms have been implemented with the help of chaotic sequence and lé vy flight. One of them is FA via lé vy flight where step size of lé vy flight has been taken from chaotic sequence. In the Bat algorithm the local search has been done via lé vy flight with chaotic step size. Chaotic sequence shows ergodicity property which helps in better searching. These two algorithms have been applied to optimize parameters of parameterized high boost filter. Entropy, number of edge pixels of the image have been used as objective criterion for measuring goodness of image enhancement. Fitness criterion has been maximized in order to get enhanced image with better contrast. From the experimental results it is clear that BA with chaotic lé vy outperforms the FA via chaotic lé vy.

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Genetic algorithms for optimal image enhancement

Cited by 106 publications

References 5 publications

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

A Chaotic Lévy flight Approach in Bat and Firefly Algorithm for Gray level image Enhancement

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