Color image with Dim regions Enhancement Using Modified Histogram Equalization Algorithm

Kareem, Hana H.

doi:10.22401/jnus.15.3.15

Cited by 4 publications

(3 citation statements)

References 4 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

“…As the quality of fundus images is poor, image enhancement technique is applied to get the "primal sketch" of retinal image. The color images have been enhanced by modified histogram equalization (MHE) algorithm [7]. On the lightness component in YIQ color space (Y component), the adaptive histogram equalization (CLAHE) method is applied.…”

Section: Pre-processing/normalizationmentioning

confidence: 99%

Early detection of retinopathy of prematurity stage using deep learning approach

Mulay

Ram

Sivaprakasam

et al. 2019

Medical Imaging 2019: Computer-Aided Diagnosis

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Retinopathy of Prematurity (ROP) is a fibrovascular proliferative disorder, which affects the developing peripheral retinal vasculature of premature infants. Early detection of ROP is possible in stage 1 and stage 2 characterized by demarcation line and ridge with width which separates vascularised retina and the peripheral retina. To detect demarcation line/ ridge from neonatal retinal images is a complex task because of low contrast images.In this paper we focus on detection of ridge, the important landmark in ROP diagnosis, using Convolutional Neural Network(CNN). Our contribution is to use a CNN-based model Mask R-CNN for demarcation line/ridge detection allowing clinicians to detect ROP stage 2 better. The proposed system applies a pre-processing step of image enhancement to overcome poor image quality. In this study we use labelled neonatal images and we explore the use of CNN to localize ridge in these images. We used a dataset of 220 images of 45 babies from the KIDROP project. The system was trained on 175 retinal images with ground truth segmentation of ridge region. The system was tested on 45 images and reached detection accuracy of 0.88, showing that deep learning detection with pre-processing by image normalization allows robust detection of ROP in early stages.

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