Secret Image Restoration With Convex Hull and Elite Opposition-Based Learning Strategy

Abstract

Digital images are easily corrupted during transmission. Most image denoising methods cannot perform well on restoring the secret image extracted from a corrupted stego image. To deal with this issue, we propose a new secret image restoration method with convex hull and elite opposition-based learning strategy. Specifically, the pixel distortion values of the corrupted secret image are calculated and used to classify the pixels into trustable pixels or untrusted pixels. For an untrusted pixel, a convex hull is generated by its context trustable pixels due to the irregular distribution of trustable pixels. The untrusted pixel in the convex hull is restored by the trustable pixels within the convex hull. The other untrusted pixels are restored using elite opposition-based learning strategy. The experimental results show that the proposed method outperforms some state-of-the-art methods regarding recovered secret image quality.