Edge Adaptive Noise Reduction Algorithm Based on Human Visual Characteristics

Nonaka, Yuichi; Yoshida, Daisuke

doi:10.3169/itej.69.j98

Cited by 2 publications

(4 citation statements)

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“…By changing the noise-reduction level pixel-by-pixel based on the human visual characteristics in the frequency domain, it has been possible to improve subjective image quality. Furthermore, human visual characteristics for chroma noise were experimentally investigated [10]. We confirmed the effectiveness of filtering-based denoising methods using human visual characteristics for chroma signals.…”

Section: Introductionsupporting

confidence: 60%

“…We have previously shown that the use of human visual characteristics for denoising is effective through experimental evaluation [9] [10]. The relational expression, which represents the visibility of luminance noise, was derived through a subjective evaluation test [9].…”

Section: Human Visual Characteristicsmentioning

confidence: 99%

“…In the same way, the relation between visibility of chroma noise and edge contrast intensity was also derived [10]. The subjective chroma noise visibility Hcn is expressed by Equations (2) and 3using the actual chroma noise intensity Ecn, luminance edge contrast intensity Eys, and chroma edge contrast intensity Ecs.…”

Section: Human Visual Characteristicsmentioning

confidence: 99%

“…The AC components decrease using a noise visibility coefficient α and edge-orientation matrix T(k, l). The decrease is expressed by Equations (10) and (11)…”

Section: Adaptive Attenuatormentioning

confidence: 99%

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Image Denoising Based on Human Visual Characteristics

Nonaka

Yoshida

Yokota

et al. 2017

Proceedings of the International Conference on Advances in Image Processing

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In this paper, we propose a denoising method for color-noise reduction considering human visual characteristics for image noise. Our related work showed that there is an inverse correlation between contrast intensity of object edge signals and visual sensitivity of image noise. Based on the inverse correlation, our new method changes the noise-reduction level pixel-by-pixel in frequency domain using 2-dimensional discrete cosine transform (2D-DCT). We also considered the edge orientation of an object and the similarity between a target pixel and its neighboring pixels in determining the noise-reduction level. The evaluation results through image simulation showed that our proposed method is effective in achieving both high resolution and low noise.

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Section: Introductionsupporting

confidence: 60%

Section: Human Visual Characteristicsmentioning

confidence: 99%

Section: Human Visual Characteristicsmentioning

confidence: 99%

“…The AC components decrease using a noise visibility coefficient α and edge-orientation matrix T(k, l). The decrease is expressed by Equations (10) and (11)…”

Section: Adaptive Attenuatormentioning

confidence: 99%

See 2 more Smart Citations