The Highly Lose Image Inpainting Method Based on Human Vision

Abstract

Currently, noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to restore damaged image data effectively, we propose a novel image inpainting technique based on wavelet transformation. The primary feature of our proposed technique is to separate the given image into two principal components which encompass image texture and color respectively. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The experimental results were perfect, even if the distortion portions of the repaired images were higher than 90%.