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Previously, we proposed a learning-based super-resolution method using the TV regularization method, which significantly reduced image processing time by removing database redundancy. However, there was a problem when noise appeared in reconstructed images because of an excessive reduction in database redundancy. In this paper, we propose a new learning-based super-resolution method, where noise is...
In this paper, we propose an effective image compression noise reduction system based on the total variation (TV) regularization method. Our system allows the removal of MPEG-2 compression noise such as mosquito noise and blocky noise without losing picture sharpness. This method is particularly useful for HDTV terrestrial broadcasting with insufficient transmission bandwidth for noise-free HDTV pictures...
In this paper, we propose a new learning-based approach for super resolution image reconstruction utilizing total variation regularization method. By using the total variation (TV) regularization decomposition, we obtain the structure component which consists of edge component and the texture component which does not include edge component of the image. We use the texture component for the learning-based...
Super resolution is not only a key word in active research but also has become a sale point for the recent consumer product such as HDTV. Among a lot of proposals for super resolution image reconstruction, the total variation regularization (TV) method seems to be the most successful approach with sharp edge preservation and no artifacts. The TV regularization method still has two problems. One is...
The resolution conversion (up-scaling) technique is important in HDTV displays, because there are still many standard definition television (SDTV) contents. A number of approaches which presumes high frequency components by nonlinear processing have been studied. The problem of these methods is that they augment the noise in the flat areas of the picture. In this paper, we propose a new resolution...
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