Definition of computational imaging

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Photonics Dictionary

computational imaging: Computational imaging refers to the use of computational techniques, algorithms, and hardware to enhance or enable imaging capabilities beyond what traditional optical systems can achieve. It involves the integration of digital processing with imaging systems to improve image quality, extract additional information from captured data, or enable novel imaging functionalities.

Principles: Computational imaging combines optics, digital signal processing, and algorithms to manipulate and enhance images. It leverages advanced computational techniques to overcome limitations of traditional optics, such as limited resolution, depth of field, and dynamic range. By processing raw image data computationally, computational imaging systems can extract more information, enhance image quality, and perform tasks such as 3D reconstruction, super-resolution, and image fusion.

Techniques and algorithms:

Multi-frame processing: Combining information from multiple frames or exposures to reduce noise, enhance resolution, or extend dynamic range.

Deconvolution: Computational algorithms to remove blur or aberrations introduced by the imaging system, improving image sharpness and clarity.

Super-resolution: Using computational techniques to enhance image resolution beyond the physical limits of the imaging system.

Light field imaging: Capturing and processing additional information about the direction and intensity of light rays to enable refocusing, depth estimation, and 3D reconstruction.

Compressive sensing: Leveraging sparsity or compressibility in image data to reconstruct high-quality images from limited measurements or incomplete data.

Machine learning: Utilizing machine learning algorithms, such as deep neural networks, for tasks such as image denoising, inpainting, and image synthesis.

Applications:

Medical imaging: Computational imaging techniques are used in medical imaging modalities such as MRI, CT, PET, and optical coherence tomography (OCT) to improve image quality, reduce scan times, and enable advanced diagnostic capabilities.

Consumer photography: Computational photography features, such as HDR (High Dynamic Range), portrait mode, and night mode, enhance image quality and enable creative effects in smartphone cameras and digital cameras.

Remote sensing: Computational imaging is used in satellite and aerial imaging systems to enhance spatial resolution, perform image fusion, and extract information about the Earth's surface for applications such as agriculture, urban planning, and environmental monitoring.

Microscopy: Computational techniques enhance the resolution, contrast, and depth of field in microscopy systems, enabling detailed imaging of biological samples and nanoscale structures.

Challenges and considerations:

Computational complexity: Some techniques require significant computational resources, limiting real-time processing or requiring specialized hardware.

Data requirements: Certain techniques, such as machine learning-based approaches, may require large amounts of training data and computational resources for training and inference.

Robustness: Computational imaging algorithms should be robust to noise, artifacts, and variations in input data to ensure reliable performance across different imaging conditions and applications.

In summary, computational imaging combines optics and digital processing to enhance imaging capabilities, extract additional information from captured data, and enable novel functionalities in various fields such as medicine, photography, remote sensing, and microscopy. By leveraging advanced computational techniques and algorithms, computational imaging systems can overcome limitations of traditional optics and achieve superior image quality, resolution, and functionality.