Single Image Super-Resolution Based on Modified Interpolation Method Using MLP and DWT

Abstract

Nowadays Super Resolution (SR) is a trending term in many fields of image processing area where resolution describes the degree of details an image holds like, pixel count, sharpness and clarity. In many applications captured images are of low quality known as low resolution (LR) image, these images hold very less details. Therefore there is a need to convert such LR images to High resolution (HR) images which can be obtained by various SR methods like, interpolation methods, frequency domain based methods, reconstruction based methods and learning based methods. HR images obtained from interpolation methods contain reconstruction artifacts like edge blurs, edge halos and ringing effects, whereas learning based methods produce good results but predict unknown HR pixel values based only on input LR/HR image pairs. Thus, to overcome above mentioned disadvantages we proposed modified interpolation method for obtaining HR image, which predicts unknown HR pixel values from interpolated LR patch and corresponding HR patch using multilayer perceptron (MLP) and discrete wavelet transform (DWT). Experimental results subjectively and objectively show that for considered test images corresponding HR images obtained by our proposed method are of better quality than classical bicubic, Chopade et al. and Man et al. method.