Similarity based anisotropic diffusion filter

Tanyeri, Ufuk; İncetaş, Mürsel Ozan; Demi̇rci̇, Recep

doi:10.1109/siu.2016.7496011

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…Additionally, its faster and more efficient operation is an important feature of the method [34]. Pixel similarity has been a popular approach used in many studies, such as edge detection [35], segmentation [36,37], and noise filters [38]. In a study conducted in 2017, pixel similarity was applied to 200 images from the BSDS database.…”

Section: Introductionmentioning

confidence: 99%

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Image interpolation with spiking neural network based pixel similarity

Kılıçaslan

2024

SIViP

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Image interpolation is an important topic in the field of image processing. It is defined as the process of transforming low-resolution images into high-resolution ones using image processing methods. Recent studies on interpolation have shown that researchers are focusing on successful interpolation techniques that preserve edge information. Therefore, the edge detection phase plays a vital role in interpolation studies. However, these approaches typically rely on gradient-based linear computations for edge detection. On the other hand, non-linear structures that effectively simulate the human visual system have gained attention. In this study, a non-linear method was developed to detect edge information using a pixel similarity approach. Pixel similarity-based edge detection approach offers both lower computational complexity and more successful interpolation results compared to gradient-based approaches. 1D cubic interpolation was applied to the pixels identified as edges based on pixel similarity, while bicubic interpolation was applied to the remaining pixels. The algorithm was tested on 12 commonly used images and compared with various interpolation techniques. The results were evaluated using metrics such as SSIM and PSNR, as well as visual assessment. The experimental findings clearly demonstrated that the proposed method outperformed other approaches. Additionally, the method offers significant advantages, such as not requiring any parameters and having competitive computational cost.

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Section: Introductionmentioning

confidence: 99%

“…In their studies, they proposed a prediction model with high accuracy using the pixel similarity approach [39]. Another significant study is the proposal of using pixel similarity as a noise filter [38]. In [35], the distance values of pixels to their neighbors were evaluated as the diffusion coefficient.…”

Section: Introductionmentioning

confidence: 99%

Image interpolation with spiking neural network based pixel similarity

Kılıçaslan

2024

SIViP

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“…The technique proposed by Demirci has become a popular approach because it is non-linear and produces faster results than statistical edge detection models. Therefore, the pixel similarity approach has been used in various subjects [30,31]. On the other hand, İncetaş et al developed the pixel similarity approach and automated the constant coe cient in the related approach [30].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the pixel similarity approach has been used in various subjects [30,31]. On the other hand, İncetaş et al developed the pixel similarity approach and automated the constant coe cient in the related approach [30]. Tanyeri et al used the aforementioned approach for image ltering and obtained successful results [31].…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Using Pixel Similarity

Orman

2023

Preprint

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Content-based image retrieval involves searching for the desired image from an image database. It is realized through the feature vectors obtained from the architectural image in question. Therefore, feature extraction is a crucial step. In this study, a new feature vector representation is proposed. In the proposed study, a composite feature vector is obtained by using color, edge, and gradient features. In particular, the method applied for edge detection offers a non-linear approach that simulates the human visual system well. In addition, there is no need for any parameter or user intervention in edge detection. In the study, experiments were carried out in Corel 1K and Corel 10K databases, which are frequently used in image retrieval. The proposed study was compared with 13 different methods. When the results are examined, the superiority of the method draws attention.

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Similarity based anisotropic diffusion filter

Cited by 2 publications

References 16 publications

Image interpolation with spiking neural network based pixel similarity

Image interpolation with spiking neural network based pixel similarity

Image Retrieval Using Pixel Similarity

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