Image Inpainting and Enhancement using Fractional Order Variational Model

Abstract

The intention of image inpainting is to complete or fill the corrupted or missing zones of an image by considering the knowledge from the source region. A novel fractional order variational image inpainting model in reference to Caputo definition is introduced in this article. First, the fractional differential, and its numerical methods are represented according to Caputo definition. Then, a fractional differential mask is represented in 8-directions. The complex diffusivity function is also defined to preserve the edges. Finally, the missing regions are filled by using variational model with fractional differentials of 8-directions. The simulation results and analysis display that the new model not only inpaints the missing regions, but also heightens the contrast of the image. The inpainted images have better visual quality than other fractional differential filters.

A Qualitative Report on Diffusion based Image Inpainting Models

“…Sridevi and Kumar [28] proposed fractional order nonlinear complex diffusion based on Caputo definition. In this http://journals.uob.edu.bh model, the coefficient of diffusion depends on the nonlinear complex function for the diffusion of the information of pixel from the source regions to the missing regions.…”

“…These can be classified into four groups. These are diffusion based (generally called image inpainting) [1], [2], [3], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37] texture-based (generally called texture synthesis) [38], [39], hybrid-based [40], [41], [42], [43], and learning based image inpainting models [44], [45], [46], [47]…”

An image inpainting method for object removal based on difference degree constraint

“…The first is based on the Partial Differential Equation (PDE). Its basic idea is that the missing region is filled smoothly by diffusing the effective information from the undamaged region into the damaged region at the pixel level [35]. The representative approaches include the BSCB model [5], the TV model [7], and the CDD model [6].…”

Low Dimensional Manifold Regularization Based Blind Image Inpainting and Non-Uniform Impulse Noise Recovery

“…The knowledge-driven methods include the partial differential equation (PDE)-based methods [4][5], the exemplar-based methods [6][7][8][9], and the sparserepresentation (SR)-based methods [10][11][12].…”