Image resolution enhancement using edge extraction and sparse representation in wavelet domain for real-time application

The paper presents the design and hardware implementation of novel framework for image resolution enhancement employing the wavelet domain. The principal idea of resolution enhancement consists of using edge preservation procedure and mutual interpolation between the input low-resolution (LR) image and the HF sub-band images performed via the Discrete Wavelet Transform (DWT). The LR image is used in the sparse representation for the resolution-enhancement process, employing a 1-D interpolation in set of angle directions; following, the computations of the new samples are found, estimating the missing samples. Finally, pixels are performed via the Lanczos interpolation. To preserve more edge information additional edge extraction in HF sub-bands is performed in the DWT decomposition of input image. The differences between the LL sub-band image and LR input image is used to correct the HF component, generating a significantly sharper reconstructed image. All sub-band images are used to generate the new HR image applying the inverse DWT (IDWT). Additionally, the novel framework employs a denoising procedure by using the Non-Local Means for the input LR image. An efficiency analysis of the designed and other state-of-the-art filters have been performed on the DSP TMS320DM648 by Texas Instruments through MATLAB's Simulink module and on the video card (NVIDIA® Quadro® K2000), showing that novel SR procedure can be used in real-time processing applications. Experimental results have confirmed that implemented framework outperforms existing SR algorithms in terms of objective criteria (PSNR, MAE and SSIM) as well as in subjective perception, justifying better image resolution.