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We present a learning-based single image super-resolution (SISR) method to obtain a high resolution (HR) image from a single given low resolution (LR) image. Our method gives more accurate results while also testing (runs) and training faster with a smaller number of training samples compared to other methods. We posed SISR as a problem of estimating a function to predict the pixels of an HR patch...
In this paper an intelligent technique for the determination of the oral cancer stages is proposed. Fuzzy If-then rules have been defined which governs the decision of the proposed system and then trained by neural network. The system is then simulated on adaptive neurofuzzy system toolbox and results have been generated. The final results show a training error equal to 0.219, which is very close...
We formulate the problem of single image super resolution (SR) in terms of learning a single but general nonlinear function. This function takes a low resolution (LR) image patch input and predicts the high resolution (HR) image pixels corresponding to the center pixel of the patch. For training, we use a LR version of an input image, and the given image pixels as target, thus obviating the need for...
In this paper a novel learning based technique for single image super resolution (SR) is proposed. We model the relationship between available low resolution (LR) image and desired high resolution (HR) image as multi-scale markov random field (MSMRF). We re-formulate the SR problem in terms of learning the mapping between LR-MRF and HR-MRF, which is generally non-linear. Instead of learning MSMRF...
In this paper, we propose a learning based technique for imagedeblurring using artificial neural networks. We model the original image as Markov Random field and the blurred image as degraded version of the original MRF. We do not make any prior assumptions for the blur kernel and develop the proposed algorithm by taking into account the space varying nature of the blur kernel. We re-formulate the...
We develop a wavelet domain learning based technique for single image super resolution (SISR). First, we learn a mapping between a patch of approximate coefficients (ACs) and the detail coefficients (DCs) corresponding the center location of the patch using Neural Networks. We then obtain an SR image by using an approximate version of the original image (scaled as per the DWT size requirements of...
In this paper, a Neuro-Fuzzy integrated system, which is based on fuzzy inference system using on-line learning ability of neural network is presented. By using on-line learning procedure, the proposed neuro-fuzzy integrated system (NFIS) can be used to construct an input-output mapping based on fuzzy if-then rules and the tuning of the parameters of membership function. The membership functions for...
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