Alkemista Journals

Journal of Computational Imaging

The Journal of Computational Imaging is a peer-reviewed, interdisciplinary publication dedicated to advancing the science and engineering of computational imaging across its full pipeline — from physical acquisition and optical design through algorithmic processing, learning-based inference, and real-world application. The journal bridges foundational theory and practical implementation, welcoming both mature contributions and innovative early-stage research that pushes the boundaries of how images are formed, reconstructed, and interpreted.

The journal publishes original research spanning the three pillars of the field:

Computational Imaging Methods and Models — including sparse, low-rank, and low-dimensional representations; statistical and graphical image models; machine learning and deep learning approaches; optimization-based inversion and reconstruction; multi-image methods and sensor fusion; super-resolution and inpainting; novel regularization strategies; and performance assessment with uncertainty quantification.

Computational Imaging Modalities — encompassing computational photography, microscopic and nanoscopic imaging, spectral and hyperspectral imaging, tomographic imaging, magnetic resonance imaging, acoustic and ultrasound imaging, radar and microwave imaging, lidar, seismic imaging, and coherent, holographic, and speckle imaging.

Computational Imaging Hardware and Algorithms — covering high-performance and fast computing, integrated hardware-algorithm co-design, novel sensors and acquisition strategies, and the systems-level challenges that arise when theory meets physical instrumentation.

The Journal of Computational Imaging serves a global community of researchers in applied mathematics, electrical engineering, computer science, physics, and the life and earth sciences who share a commitment to rigorous, reproducible, and impactful imaging science.

Disciplines

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