An enhanced model for inpainting on digital images using dynamic masking

In the digital world, inpainting is the algorithm used to replace or reconstruct lost, corrupted, or deteriorated parts of image data. Of the various proposed inpainting methods, convolutional methods are the simplest and most efficient. In this paper, an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the lost or damaged parts of an image. In the proposed algorithm, a mask image is created dynamically to detect the image area to inpaint where most of the algorithms detect the missing parts of the image manually. Studies confirm the simplicity and effectiveness of our method, which also produces results that are comparable to those produced using other methods.