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Recent years has witnessed an increasing interest in handling the issue of single image super-resolution (SISR) reconstruction. Many researches have demonstrated that the sparse representation based approaches, which rely on the ideal that image patches are assumed to have brief representations when expressed in the proper learned dictionaries, can lead to the state-of-the-art performance. The SISR...
Super-resolution (SR) image reconstruction has been one of the hottest research fields in recent years. The main idea of SR is to utilize complementary information from a set of low resolution (LR) images of the same scene to reconstruct a high-resolution image with more details. Under the framework of the regularization based SR, this paper presents a local structure adaptive BTV regularization based...
To get robust results under varied noise, an adaptive super-resolution (SR) method is proposed. The method runs in frame of regularization, and there will be an iteration process, the signal to noise ratio(SNR) is estimated by the local varicance of image in every iteration step, and be used as the regularization parameter to control the iteration, as the SNR is a reflection of image noise, and varied...
Multi frame super resolution (SR) reconstruction algorithms make use of complimentary information among low resolution (LR) images to yield a high resolution (HR) image. In this paper, we first present a fast partial differential equation (PDE) model for multi-frame image super resolution reconstruction. We then combine our proposed super resolution model with the local histogram equalization (LHE),...
In this paper, we present a generalized partial differential equation (PDE) method with a generalized regularization term for super-resolution. The generalized regularization term based on fractional order derivatives which can be seem as a generalization of the total variation (TV) regularization term and two order regularization term, which it is possible to obtain better super resolution results...
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