Demosaicking
54 papers with code • 0 benchmarks • 1 datasets
Most modern digital cameras acquire color images by measuring only one color channel per pixel, red, green, or blue, according to a specific pattern called the Bayer pattern. Demosaicking is the processing step that reconstruct a full color image given these incomplete measurements.
Source: Revisiting Non Local Sparse Models for Image Restoration
Benchmarks
These leaderboards are used to track progress in Demosaicking
PixelShift200
Latest papers with no code
A Joint Multi-Gradient Algorithm for Demosaicing Bayer Images
Experiments show that the algorithm in this paper has better recovery in image edges as well as texture complex regions with higher PSNR and SSIM values and better subjective visual perception compared to the traditional gradient algorithms such as BI, Cok, Hibbard, Laroche, Hamiton, while the algorithm involves only the add-subtract and shift operations, which is suitable to be implemented on the hardware platform.
Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures
This results in long training periods of such deep networks and the size of the networks is huge.
Pixel-Inconsistency Modeling for Image Manipulation Localization
Digital image forensics plays a crucial role in image authentication and manipulation localization.
Joint Demosaicing and Denoising with Double Deep Image Priors
Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras.
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems
In this work we present a novel optimization strategy for image reconstruction tasks under analysis-based image regularization, which promotes sparse and/or low-rank solutions in some learned transform domain.
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors
Our KLAP and KLAP-M methods achieved state-of-the-art demosaicing performance in both synthetic and real RAW data of Bayer and non-Bayer CFAs.
Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks
This paper presents a deep learning-based spectral demosaicing technique trained in an unsupervised manner.
Learning Degradation-Independent Representations for Camera ISP Pipelines
Image signal processing (ISP) pipeline plays a fundamental role in digital cameras, which converts raw Bayer sensor data to RGB images.
Twice Binnable Color Filter Arrays
New, high resolution CMOS image sensors for mobile phones have moved beyond the once-binnable Quad Bayer and RGBW-Kodak patterns to the twice binnable Hexadeca Bayer pattern featuring 4x4 tiles of like colored pixels. Pixel binning enables high speed, low power readout in low resolution modes, and more importantly, a reduction of read noise via floating diffusion binning.
Model-based demosaicking for acquisitions by a RGBW color filter array
Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane.
