Unsharp masking image enhancement the parallel algorithm based on cross-platform

Song, Yupu; Li, Cailin; Xiao, Shiyang; Han, Xiao; Guo, Baoyun

doi:10.1038/s41598-022-21745-9

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…The technique is based on the binary mask method and the edge of the object will be highlighted, as shown in Fig. 2 [21,22]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Guided Filters in Image Processing Evidence from Feathered Mask

Jiang

2024

HSET

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As a matter of fact, with the development of the electronic computers, the technique of fast Fourier transform is used in the realm of image processing. In reality, when dealing with different situations, a variety of filters and kernels will be applied and the guided filter is one of these. With this in mind, in this study, the specific task of feathered mask of image processing will be analyzed. According to the analysis, the filters improve the details at the edge of the image mask. It shows that the guided filters perform well on edge preservation and image enhancement. Moreover, the limitations and improvement will also be raised in the article at the same time. This paper aims to raise the interest of the guided filters in the realm of image processing and give some future outlooks of this method. Overall, these results shed light on guiding further exploration of Feathered musk.

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“…The technique is based on the binary mask method and the edge of the object will be highlighted, as shown in Fig. 2 [21,22]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Guided Filters in Image Processing Evidence from Feathered Mask

Jiang

2024

HSET

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“…In digital image processing, unsharp masking is a popular image improvement technique. By drawing attention to borders and limits, it seeks to improve the appearance of sharpness and fine details in an image [50,51]. This process can be divided into three steps.…”

Section: Unsharp Maskingmentioning

confidence: 99%

Toward Lightweight Diabetic Retinopathy Classification: A Knowledge Distillation Approach for Resource-Constrained Settings

Islam,

Jony,

Hasan

et al. 2023

Applied Sciences

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Diabetic retinopathy (DR), a consequence of diabetes, is one of the prominent contributors to blindness. Effective intervention necessitates accurate classification of DR; this is a need that computer vision-based technologies address. However, using large-scale deep learning models for DR classification presents difficulties, especially when integrating them into devices with limited resources, particularly in places with poor technological infrastructure. In order to address this, our research presents a knowledge distillation-based approach, where we train a fusion model, composed of ResNet152V2 and Swin Transformer, as the teacher model. The knowledge learned from the heavy teacher model is transferred to the lightweight student model of 102 megabytes, which consists of Xception with a customized convolutional block attention module (CBAM). The system also integrates a four-stage image enhancement technique to improve the image quality. We compared the model against eight state-of-the-art classifiers on five evaluation metrics; the experiments show superior performance of the model over other methods on two datasets (APTOS and IDRiD). The model performed exceptionally well on the APTOS dataset, achieving 100% accuracy in binary classification and 99.04% accuracy in multi-class classification. On the IDRiD dataset, the results were 98.05% for binary classification accuracy and 94.17% for multi-class accuracy. The proposed approach shows promise for practical applications, enabling accessible DR assessment even in technologically underdeveloped environments.

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“…Unsharp masking is an image sharpening method that employs low pass filters to sharpen details and improve overall visibility [36]. It improves skin cancer images by accentuating high-frequency components corresponding to edges and tiny details.…”

Section: Unsharp Maskingmentioning

confidence: 99%

Abundance of Mosquito Larvae at Jagannath University and Dhaka University Campus of Dhaka, Bangladesh

Islam

Prithul

2021

Bangladesh J. Zool.

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A year-round survey on the abundance of mosquito larvae was conducted at Jagannath University - and Dhaka University - Campus from October 2018 to September 2019. A number of total 4415 mosquito larvae were collected of which 3086 were identified as Culex and 1329 were Aedes. Only 3 species belonging to 2 genera were identified in this study. At Jagannath University campus, highest number of Culex larvae was recorded in the month of August which was 167 (10.9%) and the lowest number was recorded in February which was 66 (4.3%), on the other hand, highest number of Aedes larvae was recorded in the month of July which was 137 (23.8%) and the lowest number was found in December which was 9 (1.6%). At Dhaka University campus, the highest number of Culex larvae was found in the month of April, which was 179 (11.5%) and the lowest number was found in June, which was 66 (4.3%), on the other hand, the highest number of Aedes larvae was found in the month of June which was 200 (26.5%) and lowest number was found in February which was 8 (1.06%). This study could be helpful in launching larvicidal programs in a more cost-effective way at these two urban public universities of Dhaka city. Bangladesh J. Zool. 49 (2): 321-329, 2021

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Unsharp masking image enhancement the parallel algorithm based on cross-platform

Cited by 7 publications

References 36 publications

Analysis of Guided Filters in Image Processing Evidence from Feathered Mask

Analysis of Guided Filters in Image Processing Evidence from Feathered Mask

Toward Lightweight Diabetic Retinopathy Classification: A Knowledge Distillation Approach for Resource-Constrained Settings

Abundance of Mosquito Larvae at Jagannath University and Dhaka University Campus of Dhaka, Bangladesh

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