A Novel Optimal Fuzzy System for Color Image Enhancement Using Bacterial Foraging

Hanmandlu, M.; Verma, Om Prakash; Kumar, Naveen; Kulkarni, Muralidhar

doi:10.1109/tim.2009.2016371

Cited by 151 publications

(59 citation statements)

References 13 publications

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“…They did the equalizations separately over two sub-images. Similar to BBHE, [2] proposed a method named exposed based sub-image histogram equalization (ESIHE) also divide the histogram to two parts with an objective measure called exposure and do the transformation for under-and over-exposed parts separately. Another related work is by [11] (DSIHE).…”

Section: Related Workmentioning

confidence: 99%

An Improved Enhancement Method for Infrared Images

Zhou¹,

Su²,

Yang³

et al. 2017

dtcse

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Abstract. Infrared images play important roles in the national defense and civil applications. The enhancement of the infrared images aims to improve the visual quality of images so that the result is more suitable for specific applications or an observer. In this paper, we explored the histogram equalization method which is a common technique for image enhancement, and proposed an improved enhancement method for infrared images. More specifically, we proposed a method to automatically select the threshold for histogram segmentation by an iterative method, and process the pixel brightness locally by considering the visual factor. We tested our method on different types of infrared images, and quantitatively compare our method with previous methods. The results show that out method improved visual quality of the images and also provide higher entropy and peak signal noise ratio.

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Section: Related Workmentioning

confidence: 99%

An Improved Enhancement Method for Infrared Images

Zhou¹,

Su²,

Yang³

et al. 2017

dtcse

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“…A generalized iterative fuzzy enhancement algorithm is developed 14 for the degraded images with less gray levels and low contrast and it makes use of the image quality assessment criterion based on the statistical features of the gray-level histogram of images to control the iterative procedure. 18 by correcting both luminance and saturation components of color pixels. An image is separated into the under and the overexposed regions by defining the term exposure and they are enhanced separately by GINT and power law transformation operators whose parameters are found by minimizing an objective function involving the entropy, the quality and visual factors using an evolutionary algorithm like bacterial foraging 19 .…”

Section: High Dynamic Range Color Image Enhancement Using Fuzzy Logicmentioning

confidence: 99%

High Dynamic Range Color Image Enhancement Using Fuzzy Logic and Bacterial Foraging

Verma¹,

Madasu²,

Shantaram³

2011

Def. Sc. Jl.

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High dynamic range images contain both the underexposed and the overexposed regions. The enhancement of the underexposed and the overexposed regions is the main concern of this paper. Two new transformation functions are proposed to modify the fuzzy membership values of under and the overexposed regions of an image respectively. For the overexposed regions, a rectangular hyperbolic function is used while for the underexposed regions, an S-function is applied. The shape and range of these functions can be controlled by the parameters involved, which are optimized using the bacterial foraging optimization algorithm so as to obtain the enhanced image. The hue, saturation, and intensity (HSV) color space is employed for the purpose of enhancement, where the hue component is preserved to keep the original color composition intact. This approach is applicable to a degraded image of mixed type. On comparison, the proposed transforms yield better results than the existing transformation functions 17 for both the underexposed and the overexposed regions.

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“…The contrast is created by the difference in luminance reflectance from two adjacent surfaces [1], [2] and enhancement is a technique of changing the pixel intensity of the input image. The quality of image contrast reduces, due to various factors, like of poor and ambient light conditions, aperture size and shutter speed of camera [3]. Histogram equalization is a technique that improves image contrast by adjusting image intensity and is used in wide range of applications as it is a simple method can be implemented easily.…”

Section: Introductionmentioning

confidence: 99%

Standard Intensity Deviation Approach based Clipped Sub Image Histogram Equalization Algorithm for Image Enhancement

Sandeepa¹,

Jagadale²,

Bhat³

2018

ijacsa

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Abstract-The limitations of the hardware and dynamic range of digital camera have created the demand for post processing software tool to improve image quality. Image enhancement is a technique that helps to improve finer details of the image. This paper presents a new algorithm for contrast enhancement, where the enhancement rate is controlled by clipped histogram approach, which uses standard intensity deviation. Here standard intensity deviation is used to divide and equalize the image histogram. The equalization processes is applied to sub images independently and combine them into one complete enhanced image. The conventional histogram equalization stretches the dynamic range which leads to a large gap between adjacent pixels that produces over enhancement problem. This drawback is overcome by defining standard intensity deviation value to split and equalize the histogram. The selection of suitable threshold value for clipping and splitting image, provides better enhancement over other methods. The simulation results show that proposed method out performs other conventional histogram equalization (HE) methods and effectively preserves entropy.

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A Novel Optimal Fuzzy System for Color Image Enhancement Using Bacterial Foraging

Cited by 151 publications

References 13 publications

An Improved Enhancement Method for Infrared Images

An Improved Enhancement Method for Infrared Images

High Dynamic Range Color Image Enhancement Using Fuzzy Logic and Bacterial Foraging

Standard Intensity Deviation Approach based Clipped Sub Image Histogram Equalization Algorithm for Image Enhancement

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