Adaptive residual interpolation for color image demosaicking

Monno, Yusuke; Kiku, Daisuke; Tanaka, Masayuki; Okutomi, Masatoshi

doi:10.1109/icip.2015.7351528

Cited by 55 publications

(54 citation statements)

References 26 publications

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“…Compared with multilayer neural network (NN) based demosaicing technique [22], the proposed demosaicing technique exhibits an improvement of 8.82 dB in average. An improvement of 4.1 dB and 4.23dB is reported based on our proposed technique against the LSSC [29] and ARI [32]. Based on the results it is evident that our proposed demosaicing technique outperforms the existing state of art demosaicing techniques.…”

Section: Logmentioning

confidence: 66%

“…To compare the performance of the proposed demosaicing technique with the other state of art demosaicing techniques the average PSNR of the red, green, blue channels and the CPSNR results obtained is considered. The performance of the proposed demosaicing technique is compared with learned simultaneous sparse coding (LSSC) [29], Iterative Residual Interpolation (IRI) [19], multi-directional weighted interpolation and guided filter (MDWI-GF) [30], MLRI [31], similarity and color difference (SACD) based demosaicing [7], adaptive residual interpolation (ARI) [32] and multilayer neural network (NN) based demosaicing technique [22]. The NN technique proposed in [22] bears the closest similarity to the work proposed here.…”

Section: Logmentioning

confidence: 99%

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Demosaicing of Color Images by Accurate Estimation of Luminance

Prakash¹,

Prasad

Prasad³

2016

TELKOMNIKA

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Section: Logmentioning

confidence: 66%

Section: Logmentioning

confidence: 99%

Demosaicing of Color Images by Accurate Estimation of Luminance

Prakash¹,

Prasad

Prasad³

2016

TELKOMNIKA

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“…Within this framework, similar to the typical RGB Bayer filter pattern which samples at a 1:2:1 ratio, in the SRDA case the highest sampled spectral band is first interpolated and a guided filtering is subsequently applied on the rest of the bands for demosaicing [10]. Extensions of this work using residual interpolation have also been proposed [11], [12]. Advancing this process to a larger number of spectral bands and SRDA architectures, where no preferential spectral sampling is considered, requires overcoming formidable new challenges introduced by the reduction in sampling frequency.…”

Section: State-of-the-art Spectral Demosaicingmentioning

confidence: 99%

Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing

Tsagkatakis

Bloemen

Geelen

et al. 2019

IEEE Trans. Comput. Imaging

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Snapshot Spectral Imaging (SSI) is a cutting-edge technology for enabling the efficient acquisition of the spatiospectral content of dynamic scenes using miniaturized platforms. To achieve this goal, SSI architectures associate each spatial pixel with a specific spectral band, thus introducing a critical trade-off between spatial and spectral resolution. In this paper, we propose a computational approach for the recovery of high spatial and spectral resolution content from a single or a small number of exposures. We formulate the problem in a novel framework of spectral measurement matrix completion and we develop an efficient low-rank and graph regularized method for SSI demosaicing. Furthermore, we extend state-of-the-art approaches by considering more realistic sampling paradigms that incorporate information related to the spectral profile associated with each pixel. In addition to reconstruction quality, we also investigate the impact of recovery on subsequent analysis tasks like classification using state-of-the-art convolutional neural networks. We experimentally validate the merits of the proposed recovery scheme using synthetically generated data from indoor and satellite observations and real data obtained with an IMEC visible range SSI camera.

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“…Alternatively, differencing can be conducted in each tentatively estimated color channel (e.g., [7]), or their color-difference planes (e.g., [8] [9]). Many recent works perform differencing in the residual planes, which are the difference between the CFA samples and intermediately interpolated channels, and have shown promising results (e.g., [10] [11] [12] [13]).…”

Section: Introductionmentioning

confidence: 99%

Low Cost Edge Sensing for High Quality Demosaicking

Yan

Ouyang

Zuo

et al. 2019

IEEE Trans. on Image Process.

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Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. As today's camera users generally require images to be viewed instantly, demosaicking algorithms for real applications must be fast. Moreover, the associated cost should be lower than the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Based on a close look at HA's strength and weakness, we design a very low cost edge sensing scheme. Briefly, it guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 28 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy; Whereas compared to methods of similar accuracy, our method has significantly lower cost. Moreover, on test images of currently popular resolution, the quality of our algorithm is comparable to top performers, whereas its speed is tens of times faster.

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Adaptive residual interpolation for color image demosaicking

Cited by 55 publications

References 26 publications

Demosaicing of Color Images by Accurate Estimation of Luminance

Demosaicing of Color Images by Accurate Estimation of Luminance

Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing

Low Cost Edge Sensing for High Quality Demosaicking

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