Demosaicking
61 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
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Latest papers with no code
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset
This paper introduces the Raw Natural Image Noise Dataset (RawNIND), a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles.
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution
Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts.
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
To address this challenge, we propose the MambaSCI method, which leverages the Mamba and UNet architectures for efficient reconstruction of quad-Bayer patterned color video SCI.
Combining Pre- and Post-Demosaicking Noise Removal for RAW Video
Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video.
How to Best Combine Demosaicing and Denoising?
The real problem is to jointly denoise and demosaic noisy raw images.
A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging
However, snapshot mosaic imaging requires a demosaicking algorithm to fully restore the spatial and spectral details in the images.
Deep convolutional demosaicking network for multispectral polarization filter array
Imaging with a multispectral polarization filter array acquires multispectral polarization information in a snapshot.
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.