Digital holography (DH) has evolved significantly since its inception in 1948, becoming a powerful tool for quantitative phase imaging. This review discusses the principles, technical approaches, and applications of DH, highlighting its role in bridging imaging and scattering disciplines. DH enables the quantitative visualization of refractive index and thickness distributions in weakly absorbing samples, offering non-contact, dynamic, and wide-field measurement capabilities. The complex amplitude of the wavefront is described by the complex-domain, while the real-domain camera records intensity values. The process of holographic recording and reconstruction is viewed as a transformation between complex and real domains, with the mathematical and physical principles of reconstruction discussed. DH has been instrumental in various fields, including biology, materials science, and clinical sciences, due to its ability to provide detailed information about cellular structures and material properties. The development of DH has been driven by advancements in computational resources and optical technologies, leading to the emergence of techniques such as optical diffraction tomography, phase retrieval, and deep learning. These methods have enabled the reconstruction of phase information from intensity measurements, allowing for quantitative phase imaging (QPI) and 3D tomographic reconstruction. The fundamental challenge in DH is reconstructing the phase from intensity measurements, which has led to the development of various algorithms and techniques. These include phase shifting, non-interferometric methods, and iterative algorithms such as the Gerchberg-Saxton algorithm. The use of these methods has improved the accuracy and efficiency of phase retrieval, enabling applications in biomedical imaging, material characterization, and optical metrology. Recent advancements in DH have focused on improving resolution and reducing the number of measurements required for phase retrieval. Techniques such as pixel super-resolution and compressive sensing have been employed to achieve high-resolution imaging with fewer measurements. These methods leverage the sparsity of the object in certain domains to reconstruct the phase information efficiently. In conclusion, DH has become a versatile tool for quantitative phase imaging, with ongoing research aimed at enhancing its capabilities and expanding its applications in various scientific and technological fields. The integration of DH with other methodologies continues to drive innovation, offering new opportunities for research and development in biomedical and materials science.Digital holography (DH) has evolved significantly since its inception in 1948, becoming a powerful tool for quantitative phase imaging. This review discusses the principles, technical approaches, and applications of DH, highlighting its role in bridging imaging and scattering disciplines. DH enables the quantitative visualization of refractive index and thickness distributions in weakly absorbing samples, offering non-contact, dynamic, and wide-field measurement capabilities. The complex amplitude of the wavefront is described by the complex-domain, while the real-domain camera records intensity values. The process of holographic recording and reconstruction is viewed as a transformation between complex and real domains, with the mathematical and physical principles of reconstruction discussed. DH has been instrumental in various fields, including biology, materials science, and clinical sciences, due to its ability to provide detailed information about cellular structures and material properties. The development of DH has been driven by advancements in computational resources and optical technologies, leading to the emergence of techniques such as optical diffraction tomography, phase retrieval, and deep learning. These methods have enabled the reconstruction of phase information from intensity measurements, allowing for quantitative phase imaging (QPI) and 3D tomographic reconstruction. The fundamental challenge in DH is reconstructing the phase from intensity measurements, which has led to the development of various algorithms and techniques. These include phase shifting, non-interferometric methods, and iterative algorithms such as the Gerchberg-Saxton algorithm. The use of these methods has improved the accuracy and efficiency of phase retrieval, enabling applications in biomedical imaging, material characterization, and optical metrology. Recent advancements in DH have focused on improving resolution and reducing the number of measurements required for phase retrieval. Techniques such as pixel super-resolution and compressive sensing have been employed to achieve high-resolution imaging with fewer measurements. These methods leverage the sparsity of the object in certain domains to reconstruct the phase information efficiently. In conclusion, DH has become a versatile tool for quantitative phase imaging, with ongoing research aimed at enhancing its capabilities and expanding its applications in various scientific and technological fields. The integration of DH with other methodologies continues to drive innovation, offering new opportunities for research and development in biomedical and materials science.