Deep Joint Demosaicking Using Convolution Neural Network

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Zahid Warne, Viraj Vaidya, Karan Matke, H. B. Ghorpade, S. S. Bhosale, C. G. Patil


Digital images are playing very important role in research and technology which are used in face recognition, automatic license plate recognition, finger print recognition, signature recognition, satellite television, magnetic resonance imaging, computer tomography etc. Images are used in various fields like medical and education but images often degraded by noise. Noise can occur during image acquisition, transmission, reproduction etc. If the images are corrupted by noise then the quality of images will be reduced. To retain the original image from the noise corrupted image denoising techniques are used. Denoising means removal of unwanted information from an image. Image denoising model is used to remove the edges when preserving the edges. Generally the Gaussian and salt Pepper noise occurred in images of different quality due to random variation of pixel values. To denoise these images, it is necessary to apply various filtering techniques. So far there are lots of filtering methods. The aim of this work to eliminate the Gaussian and salt Pepper noise using Convolutional Neural Network (CNN).

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