Liu et al., 2020 - Google Patents
32‐2: Improvement in Directional Cubic Convolution Image InterpolationLiu et al., 2020
- Document ID
- 1436840842888563171
- Author
- Liu X
- Sun J
- Yan G
- Lu Y
- Xue Y
- Li G
- Chen L
- Zhang H
- Publication year
- Publication venue
- SID Symposium Digest of Technical Papers
External Links
Snippet
Compared with traditional methods, edge‐based interpolation algorithm can generate high‐ resolution images with rare edge artifacts. However complex textured regions produce morphological distortion at the same time, resulting in a HD image looks unnatural. In this …
- QOSSAOTZNIDXMA-UHFFFAOYSA-N Carbodicyclohexylimide data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 C1CCCCC1N=C=NC1CCCCC1 0 abstract description 16
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/403—Edge-driven scaling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4084—Transform-based scaling, e.g. FFT domain scaling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- 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
- G06T2207/20172—Image enhancement details
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
- 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
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8908989B2 (en) | Recursive conditional means image denoising | |
| CN104036479B (en) | Multi-focus image fusion method based on non-negative matrix factorization | |
| CN106169174B (en) | A method for image enlargement | |
| CN112801904A (en) | Hybrid degraded image enhancement method based on convolutional neural network | |
| Mahajan et al. | Adaptive and non-adaptive image interpolation techniques | |
| CN113963009B (en) | Local self-attention image processing method and system based on deformable block | |
| Guo et al. | Multiscale semilocal interpolation with antialiasing | |
| Ren et al. | Similarity modulated block estimation for image interpolation | |
| Hung et al. | Image interpolation using convolutional neural networks with deep recursive residual learning | |
| CN110136061B (en) | Resolution improving method and system based on depth convolution prediction and interpolation | |
| Barzigar et al. | A video super-resolution framework using SCoBeP | |
| Liu et al. | 32‐2: Improvement in Directional Cubic Convolution Image Interpolation | |
| EP2948920A1 (en) | Method and apparatus for performing single-image super-resolution | |
| Sun et al. | An Edge-Guided Weighted Image Interpolation Algorithm | |
| Fan et al. | Single image super resolution method based on edge preservation | |
| Wu et al. | Non‐uniform image blind deblurring by two‐stage fully convolution network | |
| Wei et al. | RSAN: Residual subtraction and attention network for single image super-resolution | |
| Liu et al. | Image super-resolution reconstruction based on disparity map and CNN | |
| CN111383172B (en) | Training method and device of neural network model and intelligent terminal | |
| Moses et al. | Area efficient lagrange image up scaling architecture for multimedia applications | |
| Amanatiadis et al. | Fuzzy area-based image scaling | |
| Zhiwei et al. | An image zooming technique based on the relative color difference of pixels | |
| Hajizadeh et al. | Improvement of image zooming using least directional differences based on linear and cubic interpolation | |
| CN114240760B (en) | A video image non-integer multiple super-resolution reconstruction device and method | |
| Rohith et al. | Performance analysis of satellite image super resolution using deep learning techniques |