An Efficient Adaptive Interpolation for Bayer CFA Demosaicking

Yang, Bin; Wang, Dongsheng

doi:10.1007/s11220-019-0259-6

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Nallaperumal et al [36] used a novel adaptive edge protection, edge-directed interpolation technique to reproduce the color of Bayer mosaic images. Yang et al [37] proposed a block edge estimation method considering all color channels. Sun et al [38] proposed a hybrid demosaicking algorithm based on fuzzy edge strength and residual interpolation (RI) methods.…”

Section: Model-based Methodsmentioning

confidence: 99%

Towards a Novel Generative Adversarial Network-Based Framework for Remote Sensing Image Demosaicking

Guo,

Zhang,

Jin

2024

Remote Sensing

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During satellite remote sensing imaging, the use of Bayer mode sensors holds significant importance in saving airborne computing resources and reducing the burden of satellite transmission systems. The demosaicing techniques play a key role in this process. The integration of Generative Adversarial Networks (GANs) has garnered significant interest in the realm of image demosaicking, owing to their ability to generate intricate details. However, when demosaicing mosaic images in remote sensing techniques, GANs, although capable of generating rich details, often introduce unpleasant artifacts while generating content. To address this challenge and differentiate between undesirable artifacts and realistic details, we have devised a novel framework based on a Progressive Discrimination Strategy within a Generative Adversarial Network architecture for image demosaicking. Our approach incorporates an artifact-weighted Location Map refinement technique to guide the optimization process towards generating authentic details in a stable and precise manner. Furthermore, our framework integrates a global attention mechanism to boost the interaction of spatial-channel information across different dimensions, thereby enhancing the performance of the generator network. Moreover, we conduct a comparative analysis of various prevalent attention mechanisms in the context of remote sensing image demosaicking. The experimental findings unequivocally demonstrate that our proposed methodology not only achieves superior reconstruction accuracy on the dataset but also enhances the perceptual quality of the generated images. By effectively mitigating artifacts and emphasizing the generation of true details, our approach represents a significant advancement in the field of remote sensing image demosaicking, promising enhanced visual fidelity and realism in reconstructed images.

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Section: Model-based Methodsmentioning

confidence: 99%

Towards a Novel Generative Adversarial Network-Based Framework for Remote Sensing Image Demosaicking

Guo,

Zhang,

Jin

2024

Remote Sensing

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“…Denoising and demosaicking are two fundamental and correlated tasks in camera image signal processing (ISP) pipeline with a single chip of CMOS/CCD sensor [9,20,29,45,46]. Considering that performing denoising and demosaicking separately may accumulate errors for image restoration, JDD has been widely studied in recent years [7,11,13,15,23,28,36].…”

Section: Joint Denoising and Demosaickingmentioning

confidence: 99%

“…Color demosaicking [9,20,29,45,46] recovers the missing color components from the color-filter-array (CFA) data collected by the single-chip CCD/CMOS sensor, while denoising [21,31,48,49] removes the noise in image data caused by the photon arrival statistics and the imprecision in readout circuitry. Since the two tasks are actually correlated and can be performed jointly, many joint denoising and demosaicking (JDD) algorithms have been developed [5,11,14,15,23,28], which are however focused on single image restoration.…”

Section: Introductionmentioning

confidence: 99%

A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

Guo¹,

Yang²,

Ma³

et al. 2022

Preprint

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Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. Stateof-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-toend manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available at https://github.com/ GuoShi28/2StageAlign.

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“…The captured data is inevitably corrupted by noise, especially under low-light conditions. Denoising and demosaicking play crucial roles to obtain high quality images in the camera ISP (image signal processing) pipeline, and a variety of image denoising and demosaicking methods [1], [2], [3], [4], [5] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images

Guo,

Liang,

Zhang

2021

Preprint

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Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has been achieved to perform denoising and demosaicking jointly. However, most existing CNN-based joint denoising and demosaicking (JDD) methods work on a single image while assuming additive white Gaussian noise, which limits their performance on real-world applications. In this work, we study the JDD problem for realworld burst images, namely JDD-B. Considering the fact that the green channel has twice the sampling rate and better quality than the red and blue channels in CFA raw data, we propose to use this green channel prior (GCP) to build a GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green channels are utilized to guide the feature extraction and feature upsampling of the whole image. To compensate for the shift between frames, the offset is also estimated from GCP features to reduce the impact of noise. Our GCP-Net can preserve more image structures and details than other JDD methods while removing noise. Experiments on synthetic and real-world noisy images demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.Index Terms-Real-world burst images, jointly denoising and demosaicking, green channel prior.

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An Efficient Adaptive Interpolation for Bayer CFA Demosaicking

Cited by 12 publications

References 22 publications

Towards a Novel Generative Adversarial Network-Based Framework for Remote Sensing Image Demosaicking

Towards a Novel Generative Adversarial Network-Based Framework for Remote Sensing Image Demosaicking

A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images

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