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
Most implemented papers
Plug-and-Play Image Restoration with Deep Denoiser Prior
cszn/DPIR
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Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.
Handheld Multi-Frame Super-Resolution
In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images.
CURL: Neural Curve Layers for Global Image Enhancement
sjmoran/CURL
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We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.
Replacing Mobile Camera ISP with a Single Deep Learning Model
aiff22/pynet
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The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation.
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
nickolor/Learning-to-See-in-the-Dark_and_DeepISP
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We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline.
Residual Non-local Attention Networks for Image Restoration
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Pyramid Attention Networks for Image Restoration
SHI-Labs/Pyramid-Attention-Networks
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Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.
Projected Distribution Loss for Image Enhancement
saurabh-kataria/projected-distribution-loss
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More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.
Memory-Efficient Network for Large-scale Video Compressive Sensing
BoChenGroup/RevSCI-net
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CVPR 2021
With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.
A Framework for Fast Image Deconvolution with Incomplete Observations
In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.
