- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Joint Color Space GMMs for CFA Demosaicking
摘要： We propose a patch-based algorithm for demosaicking a mosaicked color image produced by color filter arrays commonly used in acquiring color images. The proposed algorithm exploits a joint color space Gaussian mixture model (JCS-GMM) prior for jointly characterizing the patches from red, green, and blue channels of a color image. The inter channel correlations captured by the covariance matrices of Gaussian models are exploited to estimate the pixel values missing in the mosaicked image. The proposed JCS-GMM demosaicking algorithm can be seen as the GMM analogue of the Color-KSVD algorithm, which has produced impressive results in color image denoising and demosaicking. We demonstrate that our proposed algorithm achieves superior performance in the case of Kodak and Laurent Condat’s databases, and competitive performance in the case of IMAX database, when compared with state-of-the-art demosaicking algorithms.
关键词： Color filter array,demosaicking,Bayer pattern,Gaussian mixture models
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Million-Pixel Computational Imaging Model
摘要： The limited sensor size in the cameras on mobile devices leads to a low signal-to-noise ratio (SNR) during imaging. Traditionally, denoising methods are used to improve the image quality after interpolation-based demosaicking. Recently, deep learning has been employed to combine the demosaicking and denoising processes. The dual camera is designed to enhance the SNR directly by increasing the number of sensors. All of these methods have achieved success at some stage. In this study, we recast traditional imaging as independent of sampling, demosaicking, and denoising. We propose a million-pixel model to merge these three processes together in the theory of computational imaging. Our experiments demonstrated the successful application of the proposed model.
关键词： demosaicking,compressed sensing,color filter array,denoising,computational imaging
更新于2025-09-11 14:15:04
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Lightweight Deep Residue Learning for Joint Color Image Demosaicking and Denoising
摘要： Color demosaicking and image denoising each plays an important role in digital cameras. Conventional model-based methods often fail around the areas of strong textures and produce disturbing visual artifacts such as aliasing and zippering. Recently developed deep learning based methods were capable of obtaining images of better qualities though at the price of high computational cost, which make them not suitable for real-time applications. In this paper, we propose a lightweight convolutional neural network for joint demosaicking and denoising (JDD) problem with the following salient features. First, the densely connected network is trained in an end-to-end manner to learn the mapping from the noisy low-resolution space (CFA image) to the clean high-resolution space (color image). Second, the concept of deep residue learning and aggregated residual transformations are extended from image denoising and classification to JDD supporting more efficient training. Third, the design of our end-to-end network architecture is inspired by a rigorous analysis of JDD using sparsity models. Experimental results conducted for both demosaicking-only and JDD tasks have shown that the proposed method performs much better than existing state-of-the-art methods (i.e., higher visual quality, smaller training set and lower computational cost).
关键词： Convolutional neural network,Joint demosaicking and denoising,Residue Learning
更新于2025-09-09 09:28:46
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Weighted Sum-Based Color Filter Array Interpolation Using Taylor Series Cubic Approximation
摘要： In this paper, we introduce a weighted sum-based color filter array interpolation method using Taylor series cubic approximation. We use a high-order approximation to predict accurate pixel values during the reconstruction of quincunx grid-sampled green channels, and perform a weighted average-based interpolation in a large local window. We also perform prediction utilizing a color difference model in a small local window to generate additional green values. By applying the weighted sum method to the predicted green values in two local windows, green channel can be enhanced. The remaining color components, namely, the rectangular grid-sampled red and blue channels, are interpolated utilizing the weighted average method of the color difference model. Additionally, we propose a post-processing method that removes the zipper artifacts generated during the interpolation process. Experimental results demonstrate that the proposed color filter array interpolation system outperforms existing algorithms in terms of both objective and subjective performance.
关键词： image interpolation,Color filter array interpolation,Bayer pattern,Taylor series approximation,demosaicking
更新于2025-09-09 09:28:46
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[Advances in Intelligent Systems and Computing] Soft Computing for Problem Solving Volume 816 (SocProS 2017, Volume 1) || Detecting Image Forgery in Single-Sensor Multispectral Images
摘要： With the advancements in digital technology, multispectral images have found use in ?elds like forensics, remote sensing due to their ability to perceive things which were otherwise non-existent. They are used to obtain more information about terrains, land cover and in forensics as certain things like blood stains are not visible in visible spectrum. But with newly developed photo-editing softwares, they can be easily manipulated without leaving any visible clue of manipulation, but will destroy the underlying correlation between different bands. Newly developed digital cameras employ a single sensor along with multispectral ?lter array (MSFA) and then interpolate the data at other locations, hence introducing a correlation between bands. In this paper, we have proposed an algorithm that can identify the lack of correlation at tampered locations in a multispectral image and can thus help in establishing the authenticity of the given multispectral image. We show the ef?ciency of our approach with respect to the size of tampered regions in images interpolated with one the most common demosaicking algorithm—binary tree-based edge sensing (BTES).
关键词： Multispectral ?lter array (MSFA),Interpolation,MSFA demosaicking,EM algorithm,Multispectral image forgery
更新于2025-09-04 15:30:14