Salekin et al., 2018 - Google Patents
Image De-Noising Through Symmetric, Bell-Shaped, and Centered Weighted Median Filters Based Subband DecompositionSalekin et al., 2018
- Document ID
- 11981570561704326494
- Author
- Salekin S
- Agaian S
- Jahan I
- Sajal S
- Publication year
- Publication venue
- 2018 IEEE International Conference on Electro/Information Technology (EIT)
External Links
Snippet
Here, a novel image denoising algorithm that eliminates different type of noise from an image using median filter based subband decomposition. The benefit of sub-band decomposition using median transform over the wavelet decomposition method is that the …
- 238000000354 decomposition reaction 0 title abstract description 16
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/001—Image restoration
- G06T5/002—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/147—Discrete orthonormal transforms, e.g. discrete cosine transform, discrete sine transform, and variations therefrom, e.g. modified discrete cosine transform, integer transforms approximating the discrete cosine transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/20—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Easley et al. | Sparse directional image representations using the discrete shearlet transform | |
| Strela et al. | Image denoising using a local Gaussian scale mixture model in the wavelet domain | |
| US7260272B2 (en) | Method and apparatus for noise reduction using discrete wavelet transform | |
| Rabbani | Image denoising in steerable pyramid domain based on a local Laplace prior | |
| KR20190024636A (en) | Method and apparatus for reconstructing image based on neural network | |
| CN113204051A (en) | Low-rank tensor seismic data denoising method based on variational modal decomposition | |
| Khare et al. | Multilevel adaptive thresholding and shrinkage technique for denoising using Daubechies complex wavelet transform | |
| Zha et al. | Noise reduction in interferograms using the wavelet packet transform and wiener filtering | |
| Portilla et al. | Image denoising via adjustment of wavelet coefficient magnitude correlation | |
| Vimala et al. | Noise reduction based on double density discrete wavelet transform | |
| Chan et al. | Wavelets for sensing technologies | |
| Prakash et al. | Medical image denoising based on soft thresholding using biorthogonal multiscale wavelet transform | |
| Rosiles et al. | Image denoising using directional filter banks | |
| Gnanadurai et al. | Image de-noising using double density wavelet transform based adaptive thresholding technique | |
| Salekin et al. | Image De-Noising Through Symmetric, Bell-Shaped, and Centered Weighted Median Filters Based Subband Decomposition | |
| CN114331853A (en) | Single image restoration iteration framework based on target vector updating module | |
| Kaur et al. | A survey on implementation of discrete wavelet transform for image denoising | |
| Alwan | Color image denoising using stationary wavelet transform and adaptive wiener filter | |
| Adeyemi et al. | Sparse representations of images using overcomplete complex wavelets | |
| Narayan et al. | A comparative analysis for Haar wavelet efficiency to remove Gaussian and Speckle noise from image | |
| Singh et al. | Analysis of Multispectral Image Using Discrete Wavelet Transform | |
| Gupta et al. | Removal of Gaussian noise from stationary image using shift invariant wavelet transform | |
| Guangmin et al. | Image denoising with optimized subband threshold | |
| Arivazhagan et al. | A new hybrid image restoration method based on fusion of spatial and transform domain methods | |
| Shi et al. | Image sharpening via image denoising in the complex wavelet domain |