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Journal ArticleDOI

Bayer Pattern CFA Demosaicking Based on Multi-Directional Weighted Interpolation and Guided Filter

21 Jul 2015-IEEE Signal Processing Letters (IEEE)-Vol. 22, Iss: 11, pp 2083-2087
TL;DR: Simulation results verify that, the proposed framework performs better than state-of-the-art demosaicking methods in term of color peak signal-to-noise ratio (CPSNR) and feature similarity index measure (FSIM), as well as higher visual quality.
Abstract: In this letter, we proposed a new framework for color image demosaicking by using different strategies on green (G) and red/blue (R/B) components. Firstly, for G component, the missing samples are estimated by eight-direction weighted interpolation via exploiting spatial and spectral correlations of neighboring pixels. The G plane can be well reconstructed by considering the joint contribution of pre-estimations along eight interpolation directions with different weighting factors. Secondly, we estimate R/B components using guided filter with the reconstructed G plane as guidance image. Simulation results verify that, the proposed framework performs better than state-of-the-art demosaicking methods in term of color peak signal-to-noise ratio (CPSNR) and feature similarity index measure (FSIM), as well as higher visual quality.
Citations
More filters
Journal ArticleDOI
TL;DR: A color-image-dedicated reversible data hiding (RDH) algorithm is proposed to improve embedding performance by applying a guided filtering predictor and an adaptive prediction-error expansion (PEE) scheme and results demonstrate the proposed method has better performance than the state-of-the-art, color- image RDH methods.

44 citations


Cites methods from "Bayer Pattern CFA Demosaicking Base..."

  • ...Recently, the GF also has been used in camera filter array (CFA) interpolation techniques [23,24]....

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  • ...Following [23,24], finally, for further improving the prediction accuracy, a bilinear residual interpolation method can be employed to update the prediction of R1 as e R1 1⁄4 b R1 þWðR2 b R2Þ ð15Þ...

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Journal ArticleDOI
TL;DR: A novel filter is proposed, the Anisotropic Guided Filter (AnisGF), that utilises weighted averaging to achieve maximum diffusion while preserving strong edges in the image and achieves strong anisotropic filtering while preserving the low computational cost of the original guided filter.
Abstract: The guided filter and its subsequent derivatives have been widely employed in many image processing and computer vision applications primarily brought about by their low complexity and good edge-preservation properties. Despite this success, the different variants of the guided filter are unable to handle more aggressive filtering strengths leading to the manifestation of “detail halos”. At the same time, these existing filters perform poorly when the input and guide images have structural inconsistencies. In this paper, we demonstrate that these limitations are due to the guided filter operating as a variable-strength locally-isotropic filter that, in effect, acts as a weak anisotropic filter on the image. Our analysis shows that this behaviour stems from the use of unweighted averaging in the final steps of guided filter variants including the adaptive guided filter (AGF), weighted guided image filter (WGIF), and gradient-domain guided image filter (GGIF). We propose a novel filter, the Anisotropic Guided Filter (AnisGF), that utilises weighted averaging to achieve maximum diffusion while preserving strong edges in the image. The proposed weights are optimised based on the local neighbourhood variances to achieve strong anisotropic filtering while preserving the low computational cost of the original guided filter. Synthetic tests show that the proposed method addresses the presence of detail halos and the handling of inconsistent structures found in previous variants of the guided filter. Furthermore, experiments in scale-aware filtering, detail enhancement, texture removal, and chroma upsampling demonstrate the improvements brought about by the technique.

40 citations


Cites background from "Bayer Pattern CFA Demosaicking Base..."

  • ...Many other applications have benefited from edgeaware processing, including that of haze removal [22]–[25], upsampling [26]–[28], and demosaicing [29], [30]....

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Journal ArticleDOI
TL;DR: The proposed algorithm uses the channel information from several correlated neighboring pixels to reconstruct the missing color channels of each pixel and has a simple computation structure; therefore, it is appropriate for real-time hardware implementation and can be used in many real- time applications.
Abstract: An efficient edge-based technique for color filter array demosaicking is presented in this paper The proposed algorithm uses the channel information from several correlated neighboring pixels to reconstruct the missing color channels of each pixel We employ a simple edge detector to recognize the edge direction of each processing channel by using directional color differences, and an efficient color interpolator to reconstruct the missing color channels by observing the color correlation and edge information The proposed technique can prevent image blur and demosaicking artefacts; moreover, it has a fixed local window size and requires no previous training and no iterations Extensive experimental results demonstrate that the proposed technique preserves edge features and performs effectively in quantitative evaluations and visual quality The proposed algorithm has a simple computation structure; therefore, it is appropriate for real-time hardware implementation and can be used in many real-time applications

12 citations


Cites methods from "Bayer Pattern CFA Demosaicking Base..."

  • ...In [8], Wang and Jeon proposed a colour image demosaicking framework based on multi-directional weighted interpolation and guided filters....

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Proceedings ArticleDOI
14 Jun 2020
TL;DR: In this article, the authors proposed to combine denoising and demosaicing to reconstruct full-color images from a noisy color filter array (CFA) image, and carried out an extensive evaluation to find the best way to reconstruct the full color images from noisy mosaic.
Abstract: Image denoising and demosaicking are the most important early stages in digital camera pipelines. They constitute a severely ill-posed problem that aims at reconstructing a full color image from a noisy color filter array (CFA) image. In most of the literature, denoising and demosaicking are treated as two independent problems, without considering their interaction, or asking which should be applied first. Several recent works have started addressing them jointly in works that involve heavy weight CNNs, thus incompatible with low power portable imaging devices. Hence, the question of how to combine denoising and demosaicking to reconstruct full color images remains very relevant: Is denoising to be applied first, or should that be demosaicking first? In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic. We conclude that demosaicking should applied first, followed by denoising. Yet we prove that this requires an adaptation of classic denoising algorithms to demosaicked noise, which we justify and specify.

12 citations

Journal ArticleDOI
TL;DR: The result analysis presented in this paper demonstrate that the proposed de-nosing and demosaicing exhibits the better performance and it is applicable for a large variety of images.
Abstract: Color image normally contain of three main colors at the each pixel, but the digital cameras capture only one color at each pixel using color filter array (CFA). While through capturing in color image, some noise/artifacts is added. So, the both demosaicing and de-noising are the first essential task in digital camera. Here, both the technique can be solve sequentially and independently. A conventional neural network based de-noising technique has applied for the removal of noise/artifacts. Afterwards, frequency based demosaicing with the convolutional neural network based image reconstruction algorithm is apply to acquire another two missing color component. The result analysis presented in this paper demonstrate that our proposed de-nosing and demosaicing exhibits the better performance and it is applicable for a large variety of images. General Terms Image Reconstruction, Image Demosaicing, Single sensor Array, Demosaicing.

12 citations


Cites methods from "Bayer Pattern CFA Demosaicking Base..."

  • ...The average values of 24 Kodak images has taken of Circ4 [43], Wang CFA [44], DDR [33], FR [33], MLRI [28] and our proposed approach CPSNR values....

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  • ...When ours one compared to Wang CFA [43] (Average CPSNR of 36.9 dB), 23.05% improvement is seems in our proposed CPSNR 14.27%.of improvement is seems, when it compared to DDR [33] that average CPSNR value is 41.09 dB. Similarly, improved performance has achieved as 14.33% and 10.81% when it compared from FR [33] (Average CPSNR of 41.07 dB) and MLRI [28] (Average CPSNR of 42.75 dB)....

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References
More filters
Journal ArticleDOI
TL;DR: The guided filter is a novel explicit image filter derived from a local linear model that can be used as an edge-preserving smoothing operator like the popular bilateral filter, but it has better behaviors near edges.
Abstract: In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.

4,730 citations


Additional excerpts

  • ...MDWI with no refinement ( ) [13] was also compared....

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Journal ArticleDOI
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
Abstract: Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

4,028 citations

Patent
05 Mar 1975
TL;DR: In this article, a mosaic of selectively transmissive filters is superposed in registration with a solid state imaging array having a broad range of light sensitivity, the distribution of filter types in the mosaic being in accordance with the above-described patterns.
Abstract: A sensing array for color imaging includes individual luminance- and chrominance-sensitive elements that are so intermixed that each type of element (i.e., according to sensitivity characteristics) occurs in a repeated pattern with luminance elements dominating the array. Preferably, luminance elements occur at every other element position to provide a relatively high frequency sampling pattern which is uniform in two perpendicular directions (e.g., horizontal and vertical). The chrominance patterns are interlaid therewith and fill the remaining element positions to provide relatively lower frequencies of sampling. In a presently preferred implementation, a mosaic of selectively transmissive filters is superposed in registration with a solid state imaging array having a broad range of light sensitivity, the distribution of filter types in the mosaic being in accordance with the above-described patterns.

2,153 citations

Journal ArticleDOI
TL;DR: The author begins by discussing the image formation process and examines the demosaicking methods in three groups: the first group consists of heuristic approaches, the second group formulates demosaicked as a restoration problem, and the third group is a generalization that uses the spectral filtering model given in Wandell.
Abstract: The author begins by discussing the image formation process. The demosaicking methods are examined in three groups: the first group consists of heuristic approaches. The second group formulates demosaicking as a restoration problem. The third group is a generalization that uses the spectral filtering model given in Wandell.

616 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images.
Abstract: Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called color demosaicking (CDM) is used to reconstruct the full color images. Most CDM algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant CDM errors will occur. In this paper, we exploit the image nonlocal redundancy to improve the local color reproduction result. First, multiple local direc- tional estimates of a missing color sample are computed and fused according to local gradients. Then, nonlocal pixels similar to the esti- mated pixel are searched to enhance the local estimate. An adaptive thresholding method rather than the commonly used nonlocal means filtering is proposed to improve the local estimate. This allows the final reconstruction to be performed at the structural level as op- posed to the pixel level. Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images. © 2011

391 citations


Additional excerpts

  • ...Finally, we reconstruct R plane by adding the residual to the pre-estimation....

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