A New Polarization Image Demosaicking Algorithm by Exploiting Inter-Channel Correlations With Guided Filtering

Liu, Shumin; Chen, Jiajia; Xun, Yuan; Zhao, Xiaojin; Chang, Chip-Hong

doi:10.1109/tip.2020.2998281

Cited by 35 publications

(18 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

Guo¹,

Yang²,

Ma³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Joint Denoising and Demosaickingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo¹,

Yang²,

Ma³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…they are easy to implement, but their performance is mediocre. (2) Methods of interpolating using other channels as reference images, which mainly include correlation-based interpolation methods [37][38][39][40] and residual interpolation methods [41][42][43][44][45]. They are balanced in performance and stability and are the main topic of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Two channels are selected as the input image and the guide image to generate the initial estimate using the guide filter, and interpolation is executed in the residual domain containing less high-frequency information (where the residual is the difference between the observed image and the tentatively estimated image). This method has been proven to have a better demosaicing performance than other traditional polarization interpolation methods [41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Residual Interpolation Integrated Pixel-by-Pixel Adaptive Iterative Process for Division of Focal Plane Polarimeters

Jin

Qiu

et al. 2022

Sensors

View full text Add to dashboard Cite

Residual interpolations are effective methods to reduce the instantaneous field-of-view error of division of focal plane (DoFP) polarimeters. However, their guide-image selection strategies are improper, and do not consider the DoFP polarimeters’ spatial sampling modes. Thus, we propose a residual interpolation method with a new guide-image selection strategy based on the spatial layout of the pixeled polarizer array to improve the sampling rate of the guide image. The interpolation performance is also improved by the proposed pixel-by-pixel, adaptive iterative process and the weighted average fusion of the results of the minimized residual and minimized Laplacian energy guide filters. Visual and objective evaluations demonstrate the proposed method’s superiority to the existing state-of-the-art methods. The proposed method proves that considering the spatial layout of the pixeled polarizer array on the physical level is vital to improving the performance of interpolation methods for DoFP polarimeters.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

A New Polarization Image Demosaicking Algorithm by Exploiting Inter-Channel Correlations With Guided Filtering

Abstract: A new polarization image demosaicking algorithm by exploiting inter-channel correlations with guided filtering

Cited by 35 publications

References 44 publications

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

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

Residual Interpolation Integrated Pixel-by-Pixel Adaptive Iterative Process for Division of Focal Plane Polarimeters

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

Contact Info

Product

Resources

About