A Generalized Unsharp Masking Algorithm

Deng, Guang

doi:10.1109/tip.2010.2092441

Cited by 266 publications

(36 citation statements)

References 27 publications

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“…In this method, a fraction of the highpass filtered version of the image is added to the original image to enhance sharpness along the edges and therefore to enhance the contrast of the image. Unsharp Masking is a popularly used image processing technique [15]–[17] and also has been extensively used in medical imaging [18], [19].…”

Section: Save (Saliency-aided Visual Enhancement) Methodsmentioning

confidence: 99%

Efficacy Evaluation of SAVE for the Diagnosis of Superficial Neoplastic Lesion

Deeba

Mohammed

Bui

et al. 2017

IEEE J. Transl. Eng. Health Med.

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The detection of non-polypoid superficial neoplastic lesions using current standard of white light endoscopy surveillance and random biopsy is associated with high miss rate. The subtle changes in mucosa caused by the flat and depressed neoplasms often go undetected and do not qualify for further investigation, e.g., biopsy and resection, thus increasing the risk of cancer advancement. This paper presents a screening tool named the saliency-aided visual enhancement (SAVE) method, with an objective of highlighting abnormalities in endoscopic images to detect early lesions. SAVE is a hybrid system combining image enhancement and saliency detection. The method provides both qualitative enhancement and quantitative suspicion index for endoscopic image regions. A study to evaluate the efficacy of SAVE to localize superficial neoplastic lesion was performed. Experimental results for average overlap index >0.7 indicated that SAVE was successful to localize the lesion areas. The area under the receiver-operating characteristic curve obtained for SAVE was 94.91%. A very high sensitivity (100%) was achieved with a moderate specificity (65.45%). Visual inspection showed a comparable performance of SAVE with chromoendoscopy to highlight mucosal irregularities. This paper suggests that SAVE could be a potential screening tool that can substitute the application of burdensome chromoendoscopy technique. SAVE method, as a simple, easy-to-use, highly sensitive, and consistent red flag technology, will be useful for early detection of neoplasm in clinical applications.

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Section: Save (Saliency-aided Visual Enhancement) Methodsmentioning

confidence: 99%

Efficacy Evaluation of SAVE for the Diagnosis of Superficial Neoplastic Lesion

Deeba

Mohammed

Bui

et al. 2017

IEEE J. Transl. Eng. Health Med.

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“…Instead, the degraded images are processed by traditional image processing methods in order to remove noise and enhance the color, sharpness, or contrast. Image enhancement can be achieved by techniques such as the Histogram Equalization (Agaian and Roopaei, 2013), Unsharp Masking (Deng, 2011), or the Probability-based (Fu et al, 2015) method. One of the approaches in image enhancement techniques is to remove uneven illumination and preform color balancing.…”

Section: Dehazingmentioning

confidence: 99%

Impact of Dehazing on Underwater Marker Detection for Augmented Reality

et al. 2018

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Underwater augmented reality is a very challenging task and amongst several issues, one of the most crucial aspects involves real-time tracking. Particles present in water combined with the uneven absorption of light decrease the visibility in the underwater environment. Dehazing methods are used in many areas to improve the quality of digital image data that is degraded by the influence of the environment. This paper describes the visibility conditions affecting underwater scenes and shows existing dehazing techniques that successfully improve the quality of underwater images. Four underwater dehazing methods are selected for evaluation of their capability of improving the success of square marker detection in underwater videos. Two reviewed methods represent approaches of image restoration: Multi-Scale Fusion, and Bright Channel Prior. Another two methods evaluated, the Automatic Color Enhancement and the Screened Poisson Equation, are methods of image enhancement. The evaluation uses diverse test data set to evaluate different environmental conditions. Results of the evaluation show an increased number of successful marker detections in videos pre-processed by dehazing algorithms and evaluate the performance of each compared method. The Screened Poisson method performs slightly better to other methods across various tested environments, while Bright Channel Prior and Automatic Color Enhancement shows similarly positive results.

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“…It is effective in finding automatic seed point and neighbors but the dimension of the mask is to change manually for different brain MRI images [7]. In [8]- [10], the Fuzzy C-means algorithm (FCM) was proposed for segmentation. After that, an expert system was introduced with predefined membership and clustered centroid to trace a landmark tissue comparing with a prior model.…”

Section: Introductionmentioning

confidence: 99%

“…After that, an expert system was introduced with predefined membership and clustered centroid to trace a landmark tissue comparing with a prior model. On the other hand, FCM is described in [8] has limitation due to its noise sensitivity and inadequacy in the detection of abnormality in brain MRI images like a tumor, edema, and cyst. One of the most acceptable and used techniques for brain MRI image classification is Artificial Neural Network (ANN).…”

Section: Introductionmentioning

confidence: 99%