Automatic Design of High-Sensitivity Color Filter Arrays With Panchromatic Pixels

Li, Jia; Bai, Chenyan; Lin, Zhouchen; Yu, Jian

doi:10.1109/tip.2016.2633869

Cited by 20 publications

(8 citation statements)

References 33 publications

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“…Their CFA maximizes the numerical stability of linear demosaicking process under aliasing and physical constraints. Li et al proposed spatio-spectral CFA design methods that are optimized for sensitivty [28], [29].…”

Section: Related Workmentioning

confidence: 99%

Color Filter Arrays for Quanta Image Sensors

Elgendy¹,

Chan

2020

IEEE Trans. Comput. Imaging

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Quanta image sensor (QIS) is envisioned to be the next generation image sensor after CCD and CMOS. In this paper, we discuss how to design color filter arrays for QIS and other small pixels. Designing color filter arrays for small pixels is challenging because maximizing the light efficiency while suppressing aliasing and crosstalk are conflicting tasks. We present an optimization-based framework which unifies several mainstream color filter array design methodologies. Our method offers greater generality and flexibility. Compared to existing methods, the new framework can simultaneously handle luminance sensitivity, chrominance sensitivity, cross-talk, antialiasing, manufacturability and orthogonality. Extensive experimental comparisons demonstrate the effectiveness of the framework. Spatial CFA Design: By suppressing the Moiré artifacts and crosstalk while keeping the demosaicing algorithm simple, Lukac and Plataniotis [20] proposed a CFA and compared it with other CFAs using a universal demosaicking method. However, their work did not provide a mathematical framework to analyze the CFA optimality. AbstractThis supplementary report provides the following additional information of the main article.

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Section: Related Workmentioning

confidence: 99%

Color Filter Arrays for Quanta Image Sensors

Elgendy¹,

Chan

2020

IEEE Trans. Comput. Imaging

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“…Here, the updates are not by the average weight of pixels around it, but by the weighted average of pixels that are most similar. Further, the weight of each pixel is dependent on the distance between its intensity grey level vectors and that of the target pixel that helps in the identification of patches within the noisy, photometrically similar image in a given reference patch [18]…”

Section: Appendix a Derivation Of Expression For Estimating Photometrmentioning

confidence: 99%

Image De-noising using Optimized Self Similar Patch based Filter

Gayathri*¹,

Christy²

2019

IJITEE

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Emerging trends in the widespread use of technology has led to proliferation of images and videos acquired and distributed through electronic devices. There is an increasing need towards capturing high fidelity images and filtering of the concomitant noise inevitable in the capture, transmission and reception of the same. In this paper, we propose an OPSS (Optimized Patch based Self Similar) filter that exploits concurrently the photometric, geometric and graphical patch similarities of the image. This model recognizes similar patches to segregate the corrupted from the uncorrupted pixels in an image and improve the performance of denoising. Photometric patch similarity is established by using Non-Local Means Decision Based Unsymmetrical Trimmed Median (NLM-DBUTM) filter, which computes weights based on the reference patch. The geometrical patch similarity is done through the K-means clustering and graphically similar patches are identified through Ant Colony Optimization (ACO) technique. These “three similarities” based models have been taken advantage of and combined to arrive at a more comprehensive and effective denoising. The results obtained through the OPSS algorithm demonstrate improved efficiency in removing Gaussian and Impulse noise. Experimental results demonstrate that our proposed study achieves good performance with respect to other denoising algorithms being compared. Experimental results are based on performance measure (evaluation parameters) through Peak Signal to Noise Ratio (PSNR), Mean squared error (MSE) and Structural Similarity Index Measure (SSIM).

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“…Because of pre-trained weights will serve as regularization to the network, we removed the sparsity regularization [41], according to [42], the loss functions are optimized with L-BFGS algorithm to achieve fastest convergence in our settings is done during pre-training and fine tuning stages.…”

Section: 24convolution Neural Network Based Image Reconstruction Amentioning

confidence: 99%

Deep Learning Approach for Image Denoising and Image Demosaicing

Prakash¹,

Prasad²,

Prasad³

2017

IJCA

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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.

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Automatic Design of High-Sensitivity Color Filter Arrays With Panchromatic Pixels

Cited by 20 publications

References 33 publications

Color Filter Arrays for Quanta Image Sensors

Color Filter Arrays for Quanta Image Sensors

Image De-noising using Optimized Self Similar Patch based Filter

Deep Learning Approach for Image Denoising and Image Demosaicing

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