Intensity and edge based adaptive unsharp masking filter for color image enhancement

Lin, S.C.F.; Wong, Chin Yeow; Jiang, Guannan; Rahman, Arifur; Ren, Tian-Ao; Kwok, Ngaiming; Shi, Hongxing; Yu, Ying-Hao; Wu, Tonghai

doi:10.1016/j.ijleo.2015.08.046

Cited by 62 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed method is compared qualitatively and quantitatively against popular image enhancement approaches. These include smoothed histogram equalization (SMHEQ) [21], adaptive image enhancement based on bi-histogram equalization (AIEBHE) [25], non-linear transfer function local approach (NTFLA) [27], adaptive gamma correction and cumulative intensity distribution (AGCCID) [29], adaptive multi-scale Retinex for image contrast enhancement (AMRICE) [31], and intensity-and edge-based adaptive unsharp masking (IEAUM) [34].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Restoration of online video ferrography images for out-of-focus degradations

Yan

et al. 2018

J Image Video Proc.

Self Cite

View full text Add to dashboard Cite

Ferrography is a technology that can be applied in inspecting features of wear particles in machines and inferring their health status. With the development of online ferrography, which employs image processing to captured wear particle images, the inspection process has become automatic. However, it is found that images captured often contain out-of-focus degradations and low brightness. A restoration framework is here proposed to mitigate this problem. The main idea is to extract object edges, magnify with a non-linear gain factor, then combine with the input image to produce an enhanced image to facilitate further analysis. Parameters adopted in the process are optimized using a metaheuristic search where the image information content and brightness are maximized. Experimental results, obtained from processing real-world wear particle images in lubricant circuits, have shown qualitative and quantitative improvements over the input images.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In order to remove uneven illuminations, the multi-scale Retinex method can be applied globally or locally on the image [31][32][33]. When it is required to expose wear particles from its backgrounds in OLVF images, techniques based on extracting and magnifying object edges can be used [34,35].…”

Section: Introductionmentioning

confidence: 99%

Restoration of online video ferrography images for out-of-focus degradations

Yan

et al. 2018

J Image Video Proc.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Specifically, we aim to analyze the impact of the dataset size and evaluate whether more training samples are needed to improve the model accuracy and stability [13], [25]. In addition, sophisticated preprocessing techniques such as adaptive unsharp masking [27], [24] (contrast image enhancement and sharpening) are worth exploring to enhance input data. To further improve model accuracy, we aim also to explore semi-supervised learning models as the ones found in [34], [7], [8], [23].…”

Section: Discussionmentioning

confidence: 99%

A First Glance to the Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning

Ariana

Calderón-Ramírez

Thang

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Photostimulable Phosphor Plates are commonly used in digital X-ray imaging for dentistry. During its usage, these plates get damaged, influencing the diagnosis performance and confidence of the dentistry professional. We propose a deep learning based classifier to discard or extend the use of photostimulable phosphor (PSP) plates based on their physical damage. The system automatically assesses, for the first time in the literature, when dentists should discard their plates. To validate our methodology, an in-house dataset is built on 25 PSP artifact masks (Carestream, CS 7600) digitally superimposed over 100 Complementary Metal-oxide-semiconductor (CMOS) periapical images (Carestream, RVG 6200) with known radiologic interpretations. From these 2500 images, unique subsets of 100 images were evaluated by 25 dentists to find periapical inflammatory lesions on the tooth. Doctors' opinion on whether the plates should be discarded or not was also collected. Stateof-the-art deep convolutional networks were tested using fivefold cross validation, yielding classification accuracies from 87% to almost 89%. Specifically, InceptionV3 and Resnet50 obtained the best performances with statistical significance. Qualitative heat-maps showed that such models can identify and employ artifacts to decide on whether to discard the PSP plate or not.

show abstract

“…Salah satu tolak ukur yang utama dalam pemrosesan citra digital untuk mencari informasi dari citra berdasarkan warna dan ketajaman merupakan tingkat ketajaman dan kehalusan citra [2]. Berbagai metode dalam perbaikan citra untuk melakukan ketajaman dan kehalusan citra yang digunkan adalah metode unsharp mask [3][4] [5].…”

Section: Pendahuluanunclassified

Perbaikan Tingkat Kekaburan Gambar Akibat Pembesaran Pada Hasil Creenshot Dengan Metode Unsharp Mask

Aripin¹

2019

mib

View full text Add to dashboard Cite

Screenshot is a display image taken from a monitor screen such as computers, tablet PCs and smartphones. The image results from the screenshot have a low level of sharpness and smoothness. If this image is enlarged, the quality becomes low like blur. To improve the quality of the image so that it is not blurred when enlarged, then the process of sharpening and smoothing. This process will improve the quality of the image to be better. The image that is processed in this research is the image screenshot. Furthermore, the image is processed using digital image processing using Matlab software. The processing stages are crop, image enhancement and unsharp mask. The image of the image enhancement and unsharp mask results are collaborated to increase and increase the sharpness and smoothness of the image in the results of the screenshot image being tested. The results of testing on this method with better sharpness quality with a comparison of images using the mean square error of 0.0627.2404%. The image of the test results can be concluded that the value of the pixels sought has a larger image size than the original and has a resolution greater than the initial image and sharpness and smoothness so that the unsharp mask method can improve the sharpness and smoothness of the image. But the changes produced using the method have not been significant enough.

show abstract

Intensity and edge based adaptive unsharp masking filter for color image enhancement

Cited by 62 publications

References 18 publications

Restoration of online video ferrography images for out-of-focus degradations

Restoration of online video ferrography images for out-of-focus degradations

A First Glance to the Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning

Perbaikan Tingkat Kekaburan Gambar Akibat Pembesaran Pada Hasil Creenshot Dengan Metode Unsharp Mask

Contact Info

Product

Resources

About