Novel Example-Based Method for Super-Resolution and Denoising of Medical Images
| Autor: | Françoise Dibos, Canh-Duong Pham, Dinh Hoan Trinh, Marie Luong, Truong Q. Nguyen, Jean-Marie Rocchisani |
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| Rok vydání: | 2014 |
| Předmět: |
business.industry
Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image processing Signal-To-Noise Ratio Non-local means Magnetic Resonance Imaging Computer Graphics and Computer-Aided Design Computer Science::Computer Vision and Pattern Recognition Digital image processing Image Processing Computer-Assisted Humans Quadratic programming Artificial intelligence Tomography X-Ray Computed business Image resolution Algorithms Software Image restoration Mathematics Feature detection (computer vision) |
| Zdroj: | IEEE Transactions on Image Processing. 23:1882-1895 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2014.2308422 |
| Popis: | In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch. For each given input low-resolution patch, its high-resolution version is estimated based on finding a nonnegative sparse linear representation of the input patch over the low-resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the input patch and the sample patches in the database. The problem of finding the nonnegative sparse linear representation is modeled as a nonnegative quadratic programming problem. The proposed method is especially useful for the case of noise-corrupted and low-resolution image. Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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