Illumination Reduction for Low Contrast Color Image Enhancement with Homomorphic Filtering Technique

Vishwakarma, Anish Kumar; Mishra, Ashutosh; Gaurav, Kumar; Katariya, Abhishek

doi:10.1109/csnt.2012.45

Cited by 9 publications

(8 citation statements)

References 3 publications

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“…But, this can be costly [5]. Dynamic models [10], illumination correction algorithms [11][12][13][14][15] and homomorphic filtering [16][17][18] are other solutions along with their pros and cons. For example, homomorphic filtering is not directly applicable for color images and it is very slow.…”

Section: Introductionmentioning

confidence: 99%

Boosting performance of face detection by using an efficient skin segmentation algorithm

Mahmoodi

Sayedi

2014

2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE)

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Skin detection plays a very essential role in many image processing applications such as face localization, face recognition, gesture recognition and human identification. A robust pre-processing skin detection algorithm can significantly increase the performance of an application in both terms of speed and accuracy. Skin segmentation is often computationally simple, though in many conditions, uneven and nonlinear illumination degrades its performance. Recently, many methods have been proposed to solve the problem, yet most of them are not applicable in different conditions and disparate illuminations. This paper proposes an algorithm which employs not only a pixel-based processing, but also an additional neighbor-based and region-based processing. This enhances the output of the algorithm. Also a novel method in which the YC b C r image is converted to a ternary image by means of a set of decisive rules is employed. In a further processing stage, a region-based algorithm is used to decide with a high certainly on the pixels which are not skin. Results demonstrate that the proposed algorithm is robust and capable of detecting skin windows comparable with state of the art algorithms.

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Section: Introductionmentioning

confidence: 99%

Boosting performance of face detection by using an efficient skin segmentation algorithm

Mahmoodi

Sayedi

2014

2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE)

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“…HSV space is nearer to human observation, the (H) refers to the spectral composition of color, saturation (S) defines the clarity of colors and (V) refers the brightness of a color or just the luminance value of the color [9] [13]. The following equations are shows the conversion of RGB values into HSV values.…”

Section: Image Enhancement Using Hsv Color Spacementioning

confidence: 99%

“…The purpose of image enhancement is to get finer details of an image and highlight the useful information. If a person acquires an image by using a digital camera or mobile, the brightness such as glowing lamp in a room or sunlight in an open air is proper to be uneven and uncontrolled [13].…”

Section: Image Enhancement Techniquesmentioning

confidence: 99%

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Dynamic Non-Linear Enhancement using Gamma Correction and Dynamic Restoration

Singh¹,

Banga²

2014

IJCA

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This paper has proposed a new integrated image enhancement algorithm by integrating non linear image enhancement technique with dynamic restoration. Image processing plays a vital role in visualization application. It improves the visibility of poor images. Different techniques have been proposed so far. To improve image quality image enhancement can selectively enhance and restrain some information about image. It is a method which decreases image noise, eliminate artifacts, and maintain details. Its purpose is to amplify certain image features for analysis, diagnosis and display. The overall objective of this paper is to propose an integrated technique which will integrate the nonlinear enhancement technique with the gamma correction and dynamic restoration technique. The proposed algorithm is implemented in MATLAB. Experimental results have shown quite significant results over the available methods.

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“…One of the most common defects of photographic or digital images is poor contrast resulting from reduced, and nonlinear, image amplitude range [11] .Producing digital images with good contrast and detail is a strong requirement in several areas. The principle objective of enhancement is to process an image so that the result is more suitable than the original image.Image enhancement approaches fall into two broad categories: spatial domain methods and frequency domain methods.…”

Section: Introductionmentioning

confidence: 99%

Low Contrast Gray Image Enhancement using Particle Swarm Optimization (PSO) with DWT

Pareyani¹,

Mishra²

2015

IJCA

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Low contrast image enhancement is the task of applying certain transformations to an input image such as to obtain a visually more recovered, more detailed, or less noisy output image. In this paper image enhancement is considered as an optimization problem and the algorithm Particle Swarm Optimization (PSO) along with DWT is used to solve it.The objective of the proposed algorithm is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The Entropy Gain and objective criterion has been used as a comparison parameter for proposed image enhancement method.

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Illumination Reduction for Low Contrast Color Image Enhancement with Homomorphic Filtering Technique

Cited by 9 publications

References 3 publications

Boosting performance of face detection by using an efficient skin segmentation algorithm

Boosting performance of face detection by using an efficient skin segmentation algorithm

Dynamic Non-Linear Enhancement using Gamma Correction and Dynamic Restoration

Low Contrast Gray Image Enhancement using Particle Swarm Optimization (PSO) with DWT

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