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
Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing
We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure.
Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging
xyvirtualgroup/adaptivepnp_sci
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Towards this end, in this work, we propose the online PnP algorithm which can adaptively update the network's parameters within the PnP iteration; this makes the denoising network more applicable to the desired data in the SCI reconstruction.
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow
tub-rip/event_based_image_rec_inverse_problem
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Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously.
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing
In this work, we propose an unrolled convolutional dictionary learning network (CDLNet) and demonstrate its competitive denoising and joint denoising and demosaicing (JDD) performance both in low and high parameter count regimes.
Dynamic Attentive Graph Learning for Image Restoration
jianzhangcs/dagl
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ICCV 2021
Specifically, we propose an improved graph model to perform patch-wise graph convolution with a dynamic and adaptive number of neighbors for each node.
High-dimensional Assisted Generative Model for Color Image Restoration
yqx7150/hgm
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This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks.
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution
xingwz/End-to-End-JDNDMSR
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CVPR 2021
A problem of finding a joint solution of the multiple image restoration tasks just begun to attract an increased attention of researchers.
Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor
In this paper, we tackle the challenges of joint demosaicing and denoising (JDD) on such an image sensor by introducing a novel learning-based method.
PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing
egyptdj/skyb-aim2020-public
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Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc.
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.
