In-depth study of spatial domain image fusion techniques for quality enhancement Available to Purchase

Image processing techniques are widely used in all domains of application, including digital imaging, precision agriculture, computer vision, remote sensing, medical imaging, and many more. The aforementioned applications utilize various types of images, such as RGB, Infrared, Multispectral, and so forth. The image generated by a single source, sensor, or modality is insufficient for precisely realizing the item in applications such as medical imaging and remote sensing. Image fusion provides a more effective and efficient way to produce highly useful data for human perception when used with individual input source data. Numerous image fusion techniques exist, including Laplacian pyramids, Gradient Pyramids, SF, IHS, PCA, DCT, and DWT. This study examines several spatial domain Image Fusion techniques to assess the efficacy of distinct techniques based on noise content, spectral degradation, and color distortion. Comparing the outcomes of various spatial domain methods, it is found that PCA is a good choice as its PSNR value is the largest of all spatial domain methods.