Demosaicking
55 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
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing
The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement.
Toward Moiré-Free and Detail-Preserving Demosaicking
In both applications, our model substantially alleviates artifacts such as Moir\'e and over-smoothness at similar or lower computational cost to currently top-performing models, as validated by diverse evaluations.
Sign-Coded Exposure Sensing for Noise-Robust High-Speed Imaging
We present a novel Fourier camera, an in-hardware optical compression of high-speed frames employing pixel-level sign-coded exposure where pixel intensities temporally modulated as positive and negative exposure are combined to yield Hadamard coefficients.
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization
We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the $\ell_p^p$-vector and $\mathcal S_p^p$ Schatten-matrix quasi-norms for $0\!<p\!\le1$, respectively.
NeRD: Neural field-based Demosaicking
We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns.
SDAT: Sub-Dataset Alternation Training for Improved Image Demosaicing
In data centric approaches, such as deep learning, the distribution of the dataset used for training can impose a bias on the networks' outcome.
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
The advantage of Maformer is that it can leverage the MSFA information and non-local dependencies present in the data.
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images.
Large-scale Global Low-rank Optimization for Computational Compressed Imaging
However, the computational cost has inhibited NLR from seeking global structural similarity, which consequentially keeps it trapped in the tradeoff between accuracy and efficiency and prevents it from high-dimensional large-scale tasks.
Joint Demosaicing and Deghosting of Time-Varying Exposures for Single-Shot HDR Imaging
To tackle this issue, we propose a single-shot HDR demosaicing method that takes time-varying multiple exposures as input and jointly solves both the demosaicing and deghosting problems.
