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Here we describe the Caltech Tomography Database and automatic image processing pipeline, designed to process, store, display, and distribute electron tomographic data including tilt-series, sample information, data collection parameters, 3D reconstructions, correlated light microscope images, snapshots, segmentations, movies, and other associated files.
Figure 2 A block diagram of the image processing pipeline.
We propose an entire image processing pipeline for this task with a robust joint segmentation and graph-based representation approach.
The survey by Ramanath et al. [15] provides more details on the general color image processing pipeline.
The most important parts of this image processing pipeline are demosaicing and the automatic white balance (AWB).
The approach is based on the generalized noise model that is developed by following the image processing pipeline of the digital camera.
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Our improvements are applicable for both standard as well as high-dynamic range image processing pipelines.
Statistically inspired approaches have been developed in an attempt to overcome the limitations of traditional image processing pipelines.
The second class of systems that is aiming at the correct display of HDR scenes in standard dynamic-range image processing pipelines is content-based metering.
Its modular architecture allows for configurable image processing pipelines that can be built from basic components (e.g., background subtraction, blob detection, particle tracking).
Content-based metering systems and especially fuzzy logic systems can offer a very good and versatile solution for the difficult problem of obtaining an optimal image exposure, especially for standard dynamic-range image processing pipelines.
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