Optimized Color Filter Arrays for Sparse Representation-Based Demosaicking

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

doi:10.1109/tip.2017.2679440

Cited by 15 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All result images are saved to disk to have the same quantization (i.e., 0-255) and then calculated PSNR and SSIM. Otherwise, we directly quote the evaluation result from the published paper [20][21][22]49]. For methods in [35,50], whose reconstruction is not the full image, we only evaluate within the reconstructed area.…”

Section: Reconstruction From Noise-free Datamentioning

confidence: 99%

Demosaicing by Differentiable Deep Restoration

Tang

Tan

2021

Applied Sciences

View full text Add to dashboard Cite

A mosaic of color filter arrays (CFAs) is commonly used in digital cameras as a spectrally selective filter to capture color images. The captured raw image is then processed by a demosaicing algorithm to recover the full-color image. In this paper, we formulate demosaicing as a restoration problem and solve it by minimizing the difference between the input raw image and the sampled full-color result. This under-constrained minimization is then solved with a novel convolutional neural network that estimates a linear subspace for the result at local image patches. In this way, the result in an image patch is determined by a few combination coefficients of the subspace bases, which makes the minimization problem tractable. This approach further allows joint learning of the CFA and demosaicing network. We demonstrate the superior performance of the proposed method by comparing it with state-of-the-art methods in both settings of noise-free and noisy data.

show abstract

Section: Reconstruction From Noise-free Datamentioning

confidence: 99%

Demosaicing by Differentiable Deep Restoration

Tang

Tan

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Besides these mainstream CFA design frameworks, there are a number of other CFA designs such as [23], [26], [27], [30]. On the hardware side, Biay-Cheng et al [36], [37] took into account that color filter fabrication technology lags the image sensor technology in terms of miniaturization.…”

Section: Related Workmentioning

confidence: 99%

Color Filter Arrays for Quanta Image Sensors

Elgendy¹,

Chan

2020

IEEE Trans. Comput. Imaging

View full text Add to dashboard Cite

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.

show abstract

“…Similarly, Nie et al [41] jointly optimized filters and recovered spectral images by optimizing the weights in CNN. Li et al [42] adopted sparse representation to model the pipeline of color imaging and demosaicking, and then optimized filters arrays via minimizing mutual coherence. However, all of these continuous optimizations are not suitable for the discrete combination problem involved filter selection and channel arrangement.…”

Section: Related Workmentioning

confidence: 99%

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Xie

et al. 2019

Sensors

View full text Add to dashboard Cite

Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.

show abstract

Optimized Color Filter Arrays for Sparse Representation-Based Demosaicking

Cited by 15 publications

References 50 publications

Demosaicing by Differentiable Deep Restoration

Demosaicing by Differentiable Deep Restoration

Color Filter Arrays for Quanta Image Sensors

Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Contact Info

Product

Resources

About