Image enhancement through pyramid histogram matching

Sujee, R.; Padmavathi, S

doi:10.1109/iccci.2017.8117748

Cited by 16 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In paper [2] it was shown that image quality can be enhanced by improving contrast using a histogram matching and thus texture classification can be carried out in a better and efficient way.…”

Section: Literature Surveymentioning

confidence: 99%

“…After preprocessing images, in order to feed them to YOLOv2 it is necessary to annotate the images with rectangle boxes specifying their defect area in the following format. Table [2] shows the format and sample values of center of defect, width and height of the defect with respect to the image. Figure [1] displays the sample defect images from the dataset used in training YOLOv2 and YOLOv3 Tiny.…”

Section: Data Set Creationmentioning

confidence: 99%

“…Darknet is used as the framework for training YOLO, i.e it sets the architecture of the network. Figure [2] shows the flow of the proposed method for training starting from capturing the defect, annotating the defect image, training the model using the annotated image obtained from the above step. During test phase when image is given to the trained model it gives the output as show in the last step of flow diagram.…”

Section: Object Detection Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny

Sujee

Shanthosh

Sudharsun

2020

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

The paper aims to classify the defects in a fabric material using deep learning and neural network methodologies. For this paper, 6 classes of defects are considered, namely, Rust, Grease, Hole, Slough, Oil Stain, and, Broken Filament. This paper has implemented both the YOLOv2 model and the YOLOv3 Tiny model separately using the same fabric data set which was collected for this research, which consists of six types of defects, and uses the convolutional weights which were pretrained on Imagenet dataset. Observed and documented the success rate of both the model in detecting the defects in the fabric material.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Data Set Creationmentioning

confidence: 99%

Section: Object Detection Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny

Sujee

Shanthosh

Sudharsun

2020

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…In light of this, single-image enhancement as a basic low-level vision task has attracted increasing attention from computer vision researchers and artificial intelligence companies during the past few decades. The currently available enhancement algorithms can be broadly categorized into three groups, which includes (1) histogram equalization-based enhancement methods [3][4][5]; (2) Retinex theory-based enhancement methods [6][7][8][9]; or (3) deep learning-based enhancement methods [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Progressive Two-Stage Network for Low-Light Image Enhancement

Sun

Chang

Zhao³

et al. 2021

Micromachines

View full text Add to dashboard Cite

At night, visual quality is reduced due to insufficient illumination so that it is difficult to conduct high-level visual tasks effectively. Existing image enhancement methods only focus on brightness improvement, however, improving image quality in low-light environments still remains a challenging task. In order to overcome the limitations of existing enhancement algorithms with insufficient enhancement, a progressive two-stage image enhancement network is proposed in this paper. The low-light image enhancement problem is innovatively divided into two stages. The first stage of the network extracts the multi-scale features of the image through an encoder and decoder structure. The second stage of the network refines the results after enhancement to further improve output brightness. Experimental results and data analysis show that our method can achieve state-of-the-art performance on synthetic and real data sets, with both subjective and objective capability superior to other approaches.

show abstract

“…proposed an image enhancement algorithm based on pyramid histogram matching [23]. This algorithm improves the contrast of image by using histogram matching in pyramid layer and extracting image information as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Liu

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal α value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s). INDEX TERMS Low luminance images, bilateral gamma adjustment, uneven illumination, particle swarm optimization.

show abstract

Image enhancement through pyramid histogram matching

Cited by 16 publications

References 0 publications

Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny

Fabric Defect Detection Using YOLOv2 and YOLO v3 Tiny

Progressive Two-Stage Network for Low-Light Image Enhancement

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Contact Info

Product

Resources

About