EHANCING THE IMAGE SUPER-RESOLUTION VIA U-NET ARCHITECTURE FOR IMPROVED VISUAL QUALITY.
Classical method for upscaling an image has been done using Bi-Linear Interpolation, a method which interpolates a pixel in a 2d image. This bi-linear interpolation takes 4 nearest neighbors for the current selected pixel and outputs the results based on the weighted average taken. This traditional...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 1073 - 1081 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | Classical method for upscaling an image has been done using Bi-Linear Interpolation, a method which interpolates a pixel in a 2d image. This bi-linear interpolation takes 4 nearest neighbors for the current selected pixel and outputs the results based on the weighted average taken. This traditional bilinear interpolation upscaling method leaves the scaled image blurry due to over-smoothening of neighbouring pixels i.e. averaging the pixels of the neighbors. In order to avoid the loss of fine detail, we are in need of a new technology which up scales an image without losing the quality. We implemented a deep convolutional neural network (U-Net) which is widely used in the area of Image segmentation. It helps us to upscale the image without losing the quality. We used a subset of the ImageNet dataset and Oxford-IIIT PETS dataset for training and testing the model. The prediction results are far better than the classic image upscaling methods with remarkably fine details. Thus, we think our model serves the need of a good alternative to the Classical Image Upscaling method which is used in the area of Image Processing and Image Restoration. |
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