Our end-to-end system reconstructs images and jointly accounts for demosaicking, denoising, deconvolution, and missing data reconstruction. Thanks to the separation of image model and formulation based on natural-image priors, we support both conventional and unconventional sensor designs. Examples (our results at lower right): (a) Demosaicking+denoising of a Bayer-sensor image (top: regular pipeline). (b) Demosaicking+denoising of a burst image stack (top: first frame shown). (c) Demosaicking+denoising+HDR from an interlaced exposure image (top: normalized exposure image). (d) Denoising+reconstruction of a color camera array image (top: naïve reconstruction).


Conventional pipelines for capturing, displaying, and storing images are usually defined as a series of cascaded modules, each responsible for addressing a particular problem. While this divide-and-conquer approach offers many benefits, it also introduces a cumulative error, as each step in the pipeline only considers the output of the previous step, not the original sensor data. We propose an end-to-end system that is aware of the camera and image model, enforces natural-image priors, while jointly accounting for common image processing steps like demosaicking, denoising, deconvolution, and so forth, all directly in a given output representation (e.g., YUV, DCT). Our system is flexible and we demonstrate it on regular Bayer images as well as images from custom sensors. In all cases, we achieve large improvements in image quality and signal reconstruction compared to state-of-the-art techniques. Finally, we show that our approach is capable of very efficiently handling high-resolution images, making even mobile implementations feasible.

Paper and Video

Paper [FlexISP_Heide2014.pdf (86MB)]
[FlexISP_Heide2014_lowres.pdf (1MB - images compressed unrecognisably)]

Supplemental material

[FlexISP_Supplement_Heide2014.pdf (105MB)]
Additional supplement data [FlexISP_Supplement_Heide2014.zip (369MB)]
Video [FlexISP_Heide2014.mp4 (109MB)]

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