Multi -Focus Image Fusion Based on Stationary Wavelet Transform and PCA on YCBCR Color Space

Abstract

The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this paper, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images in YCbCr color space. As the first step two-level stationary wavelet transform was applied on the Y channel of two source images. The fused Y channel is implemented by using many fusion rule techniques. The Cb and Cr channels of the source images are fused using principal component analysis (PCA). The proposed method performance is evaluated in terms of PSNR, RMSE and SSIM. The results show that the fusion quality of the proposed algorithm is better than obtained by several other fusion methods, including SWT, PCA with RGB source images and PCA with YCbCr source images.

Image Segmentation for Skin Detection

“…In computer vision, it is important to choose the right color space, as this can refine the distribution of skin color in an image. Much of the research in skin color and facial localization and detection is based on RGB, YCbCr, and hue intensity saturation (HIS) color spaces [1], [18]. In this project, a pixel-based skin discovery strategy was adopted.…”