Contrast enhancement of brightness-distorted images by improved adaptive gamma correction

Cao, Gang; Huang, Lihui; Tian, Huawei; Huang, Xianglin; Wang, Yongbin; Zhi, Ruicong

doi:10.1016/j.compeleceng.2017.09.012

Cited by 95 publications

(54 citation statements)

References 24 publications

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“…AGC technique is presented with connecting the gamma parameter by cumulative distributive function (CDF) [11]. The changed pixel intensity T(i) are calculated as 1( 2)Where the CDF of gray levels are in the applied image, indicates the stabilized gray level histogram.…”

Section: ) Agc Based Cementioning

confidence: 99%

“…Still, we get that the pursuing local normalization system aids generalization. Indicating with the action of a neuron employed with concerning kernel at position with the concerning the ReLU non linearity, the reply normalized action are provided with the expression (11) where the sum runs above "adjacent" kernel maps by the similar spatial stage, and are the entire number of kernels in these layers. To arranging the kernel maps are certainly random and decided to train before.…”

Section: Local Response Normalizationmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

Senthilkumar,

Kamarasan

2019

IJEAT

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In farming sector, diseases affected in plants are mainly accountable for the minimized profit that leads to financial loss. In case of plants, citrus is utilized as a main resource of nutrients namely vitamin C globally. But citrus diseases greatly affect the productivity as well as quality. In recent days, computer vision and image processing approaches are commonly applied for detecting and classifying the plant diseases. This paper presents a novel deep learning (DL) based citrus disease detection and classification model. A new DL based AlexNet architecture is employed for effective identification of diseases. The presented model involves four main processes namely pre-processing, segmentation, feature extraction, and classification. Initially, pre-processing takes place to improve the quality of the image. Then, the Otsu method is applied to segment the images. Next, Alex-Net model is applied as a feature extractor. Finally, random forest (RF) classifier is used to classify the different kinds of citrus diseases. Besides, adaptive gamma correction (AGC) model is applied to improve the contrast of the applied citrus images. A comprehensive experimentation takes place on Citrus Disease Image Gallery Dataset. The results are examined under several cases and the outcome ensured the effective characteristics of the presented AGC-A model

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Section: ) Agc Based Cementioning

confidence: 99%

Section: Local Response Normalizationmentioning

confidence: 99%

An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

Senthilkumar,

Kamarasan

2019

IJEAT

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show abstract

“…The result of segmentation will be significantly enhanced following the preprocessing step. The sequence of combination of image preprocessing algorithms such as contrast stretching [15], median filtering [16] and grey level converter discover a improved segmentation method which be able to detect defects from images accurately and automatically. To improve the image aspect or quality and filter out the noise to make certain the distinct contrast by contrast stretching method.…”

Section: A Image Preprocessingmentioning

confidence: 99%

Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

Aslam¹,

Santhi²,

Ramasamy³

et al. 2019

IJITEE

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An innovative approach is introduced to detect surface defects on titanium coated steel surfaces with varied size through the use of image processing techniques. This paper provides techniques which are useful to discover numerous kinds of surface defects present on coating surface. For defect detection, Firefly Algorithm (FA) based adaptive thresholding is proposed and is applied for the gray scale images. The FA ensuing nature inspired algorithm utilized expansively in support of determining various optimization problems and from the reconstructed image contours are extracted using level set method, the predictable images not including textures besides defects contours be compassed. The morphological post processing removes the noise in image and makes defects more distinguishable from the background. The speculative result persists in utilizing synchronous images of metal surface and shows that the proposed method can efficiently segment surface defects and obtain better performance than existing methods.

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“…The image contrast is the separation factor between the darkest and brightest spot in the image. The contrast stretching method [16] involves calculation of numerical information involved with every pixel of an image. The process of contrast stretching is an essential pre-processing phase in maintaining the contrast and quality of images.…”

Section: (A) Contrast Stretchingmentioning

confidence: 99%

Detection of Surface Coating Defects using Fuzzy C-Means Clustering with Firefly Optimization

Aslam*¹,

Santhi²,

Ramasamy³

et al. 2019

IJEAT

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Defect detection in metallic surface images is a challenging task in the image analysis process. The data clustering and optimization techniques have been widely used for image segmentation and the combination of these two approaches improves the output stability as well as convergence speed. In this work developed an automatic, efficient method for the detection and segmentation of coating defects in metal surfaces. The Fuzzy c-means (FCM) and Firefly algorithm (FA) are well-known and popular methods to discover the image information comprising indiscriminate objects and solves many complex problems involved in image segmentation. In this paper, proposed a new technique for the coated metal surface defect detection using the hybridization of two methods, FCM with FA (FCM-FA). The results from experiments verified the efficiency of the developed FCM with FA over comparison with three existing methods in terms of evaluation parameters of defect detection for scanned high resolution images. It can be seen from the experimental results that the incorporated algorithm has the potential to segment and identify the defected regions from the coated surface.

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Contrast enhancement of brightness-distorted images by improved adaptive gamma correction

Cited by 95 publications

References 24 publications

An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

Detection of Surface Coating Defects using Fuzzy C-Means Clustering with Firefly Optimization

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