The journal focuses on the development and analysis of computational methods for solving inverse problems in imaging. This includes techniques for image reconstruction from incomplete or noisy data, such as sparse-view computed tomography and magnetic resonance imaging. The scope also encompasses the application of deep learning, implicit neural representations, and optimization frameworks to enhance image quality and resolution in various imaging modalities.
Sparse and Compressive Sensing TechniquesImage and Signal Denoising MethodsNumerical methods in inverse problemsMedical Image Segmentation TechniquesAdvanced Image Processing TechniquesMicrowave Imaging and Scattering AnalysisMedical Imaging Techniques and ApplicationsPhotoacoustic and Ultrasonic ImagingAdvanced Vision and Imaging3D Shape Modeling and Analysis