Hybrid image inpainting using reproducing kernel Hilbert space and dragonfly inspired wavelet transform

This paper intends to propose a new inpainting model that based on Mumford Shah (MS) modelling, where the original image is gained accurately by doing inpainting process in the masked image. Here, discrete wavelet transform (DWT) is used for processing with the digital image. Further, to find the optimal filter coefficients from DWT, a renowned optimisation technique named dragonfly (DA) is used. Moreover, the smoothing of image is process via reproducing kernel Hilbert smoothing model. The proposed dragonfly optimised DWT kernel-MS (DODWTK-MS) model compares its performance with other conventional methods in terms of second derivative measure of enhancement (SDME), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), mean squared error (MSE) and edge similarity and the efficiency of the developed model is explained. Copyright © 2019 Inderscience Enterprises Ltd.