Performance analysis of peak signal-to-noise ratio and multipath source routing using different denoising method

Kannadhasan, S.; Nagarajan, R.; Sudarmani, R.; Sakkarai, Jeevitha; Asokan, Balakumar; Alagarsamy, Manjunathan

doi:10.11591/eei.v11i1.3332

Cited by 16 publications

(9 citation statements)

References 4 publications

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“…Those approaches depend upon issue change, but also, these approaches hanging around algorithmic change are those two categories of inter categorization approaches. This same primary principle behind issue transforming techniques was essential to break any interclassifying challenge down several separate classifiers [14]. Then-current singular category strategies may be employed effectively to solve this challenge.…”

Section: Proposed Methodsmentioning

confidence: 99%

Application of Automatic Identification of Ontology Hierarchical Structure for Measuring Gene Function

Trinathbasu,

Sajith

et al. 2023

Recent Trends in Data Science and Its Applications

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Knowledge within this same genetic ontology annotating has helped explain the occurrences of biological sciences, hence, being a precious resource for therapeutic development. This same genetic ontology was increasingly changing the way individuals manage, but instead understanding the biological material of systems. Designers turn everything into the amount-based methods founder upper categorization conundrum instead establish a procedure that has used these same connections throughout this same Gene Ontology structural system. It alleviates the quantification inequity of optimistic but rather detrimental coaching specimens to make it easier for genomic component prognostication. This aids in the message extraction approaches but instead establishes infrastructure. Conversely, the proposed technique improves classification discrimination by keeping but instead accentuating these same most important learning examples. Furthermore, our upper edge classifiers predicated around hierarchical branch construction consider overall connection amongst targeted categories, resolving general inconsistency among categorization outcomes and the underlying GenesMorphology framework. Their research's overall F-measure effectiveness using the general Genetic Ontology annotating dataset was 50.7% (precision: 52.7%, recall: 48.9%). These research findings show that even though their trained collection seems minimal, it may substantially increase by propagating linked information across parental and offspring branches throughout that forest architecture using geometrical dispersion. Any group or document within complex ontological architecture with any vertical connection can be classified using this same highest categorization paradigm.

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Section: Proposed Methodsmentioning

confidence: 99%

Application of Automatic Identification of Ontology Hierarchical Structure for Measuring Gene Function

Trinathbasu,

Sajith

et al. 2023

Recent Trends in Data Science and Its Applications

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“…When utilizing a median filter, each output pixel is set to the average of the neighborhood pixels of the associated input, which is similar to how an averaging filter works. The value of each output pixel, however, is instead decided by the median of the nearby pixels when using median filtering [19]. The median is significantly less vulnerable to outliers (extreme values) than the mean is.…”

Section: The Median Filtermentioning

confidence: 99%

Spatial domain noise removal filtering for low-resolution digital images

Salah,

Al-Sit,

Salah

et al. 2024

IJEECS

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<p>n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.</p>

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“…The peak signal-to-noise ratio ( PSNR ) can objectively quantify the denoising effect. NMISE represents the normalized mean squared error of integration between the denoised and noise-free data [ 53 ].

…”

Section: Experimental Analysismentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

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Performance analysis of peak signal-to-noise ratio and multipath source routing using different denoising method

Cited by 16 publications

References 4 publications

Application of Automatic Identification of Ontology Hierarchical Structure for Measuring Gene Function

Application of Automatic Identification of Ontology Hierarchical Structure for Measuring Gene Function

Spatial domain noise removal filtering for low-resolution digital images

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

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