Non-iterative joint demosaicing and super-resolution framework

Petrova, Xenya; Glazistov, Ivan; Zavalishin, Sergey; Kurmanov, V.; Lebedev, Konstantin S.; Молчанов, А. Г.; Shcherbinin, Andrey; Milyukov, G.; Kurilin, Ilya V.

doi:10.2352/issn.2470-1173.2017.17.coimg-439

Cited by 1 publication

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“…These methods were parted into two distinctive categories i.e. iterative [6] and non-iterative [7]. Non-iterative technique is based on the edge directed interpolation for improvising the performance of construction, here the exploitation takes place through the estimation of local covariance information [8] and local gradients [9] and it is based on either difference rule [10] or ratio rule of color [11].…”

Section: Figure 1de-mosaicing Processmentioning

confidence: 99%

DE-Mosaicing using Matrix Factorization Iterative Tunable Method

Tabassum*,

Gowre

2019

IJITEE

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A color image is captured though the single image sensor and it is named as the mosaicked image and this is obtained through the CFA where the pixels are arranged such that any one of the color from the given color component is recorded at every pixel. De-mosaicingis absolute reverse of mosaicking, where the process is to reconstruct the full color image from the given incomplete color samples. In past several methods of de-mosaicing have been proposed however, they have many shortcomings such as computational complexity and high computational load, not matching the original images. Hence we have proposed a methodology named as MFIT i.e. Matrix factorization Iterative Tunable approach, the main aim of this methodology is to improvise the reconstruction quality. In order to achieve the better reconstruction quality we have used the MFIT algorithm at each iteration this helps in avoiding the image artifacts and it is achieved through the image block adjustment also it reduces the computational load. Moreover in order to evaluate the proposed algorithm we have compared it with nearly 12 algorithm based on the value of PSNR and SSIM, the theoretical results and comparative analysis shows that our algorithm excels compared to the other existing method of de-mosaicing.

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Section: Figure 1de-mosaicing Processmentioning

confidence: 99%