Single Image Super Resolution using Interpolation and Discrete Wavelet Transform

An interpolation-based method, such as bilinear, bicubic, or nearest neighbor interpolation, is regarded as a simple way to increase the spatial resolution for the LR image. It uses the interpolation kernel to predict the missing pixel values, which fails to approximate the underlying image structure and leads to some blurred edges. In this work a super resolution technique based on Sparse characteristics of wavelet transform. Hence, we proposed a wavelet based super-resolution technique, which will be of the category of interpolative methods, using sparse property of wavelets. It is based on sparse representation property of the wavelets. Simulation results prove that the proposed wavelet based interpolation method outperforms all other existing methods for single image super resolution. The proposed method has 7.7 dB improvement in PSNR compared with Adaptive sparse representation and self-learning ASR-SL 1 for test image Leaves, 12.92 dB improvement for test image Mountain Lion and 7.15 dB improvement for test image Hat compared with ASR-SL 1 . Similarly, 12 improvement in SSIM for test image Leaves compared with 1 , 29 improvement in SSIM for test image Mountain Lion compared with 1 and 17 improvement in SSIM for test image Hat compared with 1 . Shalini Dubey | Prof. Pankaj Sahu | Prof. Surya Bazal "Single Image Super Resolution using Interpolation & Discrete Wavelet Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18340.pdf