Transform image enhancement

Aghagolzadeh, Sabzali; Ersoy, Okan K.

doi:10.1117/12.56095

Cited by 102 publications

(60 citation statements)

References 12 publications

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“…(8) respectively for natural color images. These non-reference image quality metrics are used to compare the performance of proposed ACCE algorithm and other existing contrast enhancement techniques such adaptive histogram equalization (AHE) [6], alpha rooting (AR) [7], multi contrast enhancement (MCE) [26], modified histogram Equalization (MHE) [27], Adaptive Contrast Enhancement Based on modified Sigmoid Function (ACEBSF) [28], Multi-contrast Enhancement with Dynamic Range Compression (MCEDRC) [29], Contrast Enhancement by Scaling (CES) [30], RGB retinex theory [31].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…On the other hand various researchers also proposed many algorithms for contrast enhancement in DCT based compressed domain such as alpha rooting (AR) [7], multi contrast enhancement (MCE) [26], modified histogram Equalization (MHE) [27], Adaptive Contrast Enhancement Based on modified Sigmoid Function (ACEBSF) [28], Multicontrast Enhancement with Dynamic Range Compression (MCEDRC) [29], Contrast Enhancement by Scaling (CES) [30], RGB retinex theory [31] and other methods for contrast enhancement [32][33][34][35]. In order to determine image quality metric, many existing image quality assessment algorithms use only limited image features.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Method for Contrast Enhancement of Natural Color Images

Lal¹,

Narasimhadhan²,

Kumar³

2015

Journal of Electrical Engineering and Technology

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-The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Method for Contrast Enhancement of Natural Color Images

Lal¹,

Narasimhadhan²,

Kumar³

2015

Journal of Electrical Engineering and Technology

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show abstract

“…It is known that the human visual detection depends on the ratio between high-frequency and low-frequency content [5]. Thus, the contrast measure can be defined as the ratio of high and low-frequency content in the bands of the DCT matrix.…”

Section: Contrast Measure In the Dct Domainmentioning

confidence: 99%

“…Our contrast measure also has a multiscale structure that corresponds with the human vision system as in [5]. Based on this contrast measure, an image enhancement algorithm for direct application to the DCT domain is developed.…”

Section: Introductionmentioning

confidence: 99%

A contrast enhancement method for compressed images

Sun

2004

IEICE Electron. Express

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“…While a large set of surveys of the different methods for image enhancements can be found in [2,58,31,68,94,66,4,1,3,71]. This section covers some of the most common image enhancement methods.…”

Section: Image Enhancementmentioning

confidence: 99%

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

Hernadez¹

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In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices.Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines.The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers.We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be v developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain.In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem.In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

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Transform image enhancement

Cited by 102 publications

References 12 publications

Automatic Method for Contrast Enhancement of Natural Color Images

Automatic Method for Contrast Enhancement of Natural Color Images

A contrast enhancement method for compressed images

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

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