PROJECT TITLE :

Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis - 2015

ABSTRACT:

Traditional sparse image models treat color image pixel as a scalar, that represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color pictures using quaternion matrix analysis. As a replacement tool for color image representation, its potential applications in many image-processing tasks are presented, together with color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the colour image as a quaternion matrix, where a quaternion-primarily based dictionary learning algorithm is presented using the K-quaternion singular price decomposition (QSVD) (generalized K-means that clustering for QSVD) methodology. It conducts the sparse basis choice in quaternion area, which uniformly transforms the channel pictures to an orthogonal color area. During this new color house, it's important that the inherent color structures will be fully preserved throughout vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of various color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.