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Found 11 papers, 5 papers with code
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns
We found that concatenating a three-channel mosaic embedding to the input image and training with a unified demosaicing architecture yields results that outperform existing Quad-Bayer and Nona-Bayer models and are comparable to Single-Bayer models.
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images
The narrowing domain gap between real and synthetic imagery makes it possible to use CG images as training data for deep learning models targeting high-level computer vision tasks, such as autonomous driving and semantic segmentation.
Day-to-Night Image Synthesis for Training Nighttime Neural ISPs
Training nightmode ISP networks relies on large-scale datasets of image pairs with: (1) a noisy raw image captured with a short exposure and a high ISO gain; and (2) a ground truth low-noise raw image captured with a long exposure and low ISO that has been rendered through the ISP.
Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata
Our experiments show that our learned sampling can adapt to the image content to produce better raw reconstructions than existing methods.
Leveraging the Availability of Two Cameras for Illuminant Estimation
In this paper, we leverage the availability of these two cameras for the task of illumination estimation using a small neural network to perform the illumination prediction.
CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks
Because of this, a number of methods have been proposed to "unprocess" nonlinear images back to a raw-RGB state.
A Little Bit More: Bitplane-Wise Bit-Depth Recovery
Imaging sensors digitize incoming scene light at a dynamic range of 10--12 bits (i. e., 1024--4096 tonal values).
Reflection Removal Using a Dual-Pixel Sensor
Reflection removal is the challenging problem of removing unwanted reflections that occur when imaging a scene that is behind a pane of glass.
As-projective-as-possible bias correction for illumination estimation algorithms
Recent work by Finlayson, Interface Focus, 2018 showed that a bias correction function can be formulated as a projective transform because the magnitude of the R, G, B illumination vector does not matter to the AWB procedure.
Revisiting Autofocus for Smartphone Cameras
The fact that different objectives exist raises the research question of whether there is a preferred objective.
Rolling Shutter Super-Resolution
But CMOS sensors that have increasingly started to replace their more expensive CCD counterparts in many applications do not respect this assumption if there is a motion of the camera relative to the scene during the exposure duration of an image because of the row-wise acquisition mechanism.
