An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics
The shrinkage function has an important effect on the image denoising results. An adaptive shrinkage function is developed in this paper to shrink the small coefficients properly for image denoising based on neighborhood characteristics. The shrinkage function is determined by the number of large co...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Slovenian Society for Stereology and Quantitative Image Analysis
2022-07-01
|
| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/2703 |
| _version_ | 1818114779314651136 |
|---|---|
| author | Ying Yang Yusen Wei |
| author_facet | Ying Yang Yusen Wei |
| author_sort | Ying Yang |
| collection | DOAJ |
| description | The shrinkage function has an important effect on the image denoising results. An adaptive shrinkage function is developed in this paper to shrink the small coefficients properly for image denoising based on neighborhood characteristics. The shrinkage function is determined by the number of large coefficients near the current signal coefficients. In this way, different shrinkage functions can be adaptively used to deal with different coefficients in the process of image denoising, instead of using fixed shrinkage functions. Experimental results show that the SNR of the image processed by the adaptive shrink function algorithm is better than that processed by the soft threshold, hard threshold, and neighborhood shrink algorithm. Moreover, compared with the traditional soft threshold, hard threshold and neighborhood shrink algorithm, the PSNR of the algorithm using adaptive shrink function increases by 3.68dB, 2.28dB and 0.61dB, respectively. In addition, the proposed new algorithms, soft threshold and hard threshold, are combined with empirical Wiener filtering and shift invariant (TI) scheme to compare their image noise reduction effects. The results show that the PSNR can be improved significantly by using the adaptive shrink function algorithm combined with empirical Wiener filtering and shift invariant (TI) scheme. |
| first_indexed | 2024-12-11T03:56:09Z |
| format | Article |
| id | doaj.art-bcdb4fd88bde4aba9d44cdc61de51687 |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-12-11T03:56:09Z |
| publishDate | 2022-07-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-bcdb4fd88bde4aba9d44cdc61de516872022-12-22T01:21:47ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652022-07-0141212113110.5566/ias.27031078An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood CharacteristicsYing Yang0Yusen Wei1Department of Electronic Engineering, Xi An University of TechnologyDepartment of Electronic Engineering, Xi an University of technology Xi An Vocational University of AutomobileThe shrinkage function has an important effect on the image denoising results. An adaptive shrinkage function is developed in this paper to shrink the small coefficients properly for image denoising based on neighborhood characteristics. The shrinkage function is determined by the number of large coefficients near the current signal coefficients. In this way, different shrinkage functions can be adaptively used to deal with different coefficients in the process of image denoising, instead of using fixed shrinkage functions. Experimental results show that the SNR of the image processed by the adaptive shrink function algorithm is better than that processed by the soft threshold, hard threshold, and neighborhood shrink algorithm. Moreover, compared with the traditional soft threshold, hard threshold and neighborhood shrink algorithm, the PSNR of the algorithm using adaptive shrink function increases by 3.68dB, 2.28dB and 0.61dB, respectively. In addition, the proposed new algorithms, soft threshold and hard threshold, are combined with empirical Wiener filtering and shift invariant (TI) scheme to compare their image noise reduction effects. The results show that the PSNR can be improved significantly by using the adaptive shrink function algorithm combined with empirical Wiener filtering and shift invariant (TI) scheme.https://www.ias-iss.org/ojs/IAS/article/view/2703image denoisingneighboring coefficientswavelet transforms |
| spellingShingle | Ying Yang Yusen Wei An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics Image Analysis and Stereology image denoising neighboring coefficients wavelet transforms |
| title | An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics |
| title_full | An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics |
| title_fullStr | An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics |
| title_full_unstemmed | An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics |
| title_short | An Adaptive Shrinkage Function for Image Denoising Based on Neighborhood Characteristics |
| title_sort | adaptive shrinkage function for image denoising based on neighborhood characteristics |
| topic | image denoising neighboring coefficients wavelet transforms |
| url | https://www.ias-iss.org/ojs/IAS/article/view/2703 |
| work_keys_str_mv | AT yingyang anadaptiveshrinkagefunctionforimagedenoisingbasedonneighborhoodcharacteristics AT yusenwei anadaptiveshrinkagefunctionforimagedenoisingbasedonneighborhoodcharacteristics AT yingyang adaptiveshrinkagefunctionforimagedenoisingbasedonneighborhoodcharacteristics AT yusenwei adaptiveshrinkagefunctionforimagedenoisingbasedonneighborhoodcharacteristics |