The conventional method of processing an image is to express multispectral images as vectors or matrices. However, the quaternion representation offers a better alternative as it contains the complete information of features of color images. Quaternion-Sparse Image Processing:
Advances in Multispectral Processing brings together the technologies, research and managerial applications of quaternion-sparse based complex algebra in image processing. The book covers the entire range of complicated tasks performed on color images, including denoising, reconstruction, classification, hallucination, feature extraction, dimension reduction, and regularization. It provides easy understanding and smooth adaptability of the basic and advanced concepts for graduate students, researchers, doctors, academics, and practitioners.

• Uncovers the innovative features of complex algebra, specifically the quaternion-sparse concept in image processing and how it can help in improving the computational efficiency of image processing
• Deals with the most common quaternion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processing
• Focuses on how evolution in algebraic concepts i.e. quaternion and sparse help in improving accuracy and efficiency of various color image restoration, reconstruction and recognition
• Illustrates how important features are extracted and complete information is stored in extracted features to help and process tasks in an easy and computationally efficient way
• Provides contributed case studies in various image processing applications and various technological evolutions in quaternion-Sparse based image processing in both literature and practical aspects on face recognition, medical image processing, biometric security and many more fields where multispectral images are used as an input to the system