Analysis of Deep Learning Architecture for Non-Uniformly Illuminated Images

Subbiah, Uma; Padmavathi, S

doi:10.1109/icict48043.2020.9112434

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…This method has strong dependence on the constructed energy functional model, and the image is prone to step effect after corrected, and the algorithm solution convergence speed is slow. The nonuniformity correction methods based on statistical matching include histogram matching [16], moment matching [17,18] and their improvement methods [19][20][21][22][23], among others, the essence of moment matching is to normalize the image [24][25][26], taking the average of all column means and column standard deviations of the whole image as reference values and bring them into the moment matching formula to complete the non-uniformity correction of recovering the true column means from the column means containing noise. These methods perform non-uniformity correction according to the difference of the numerical statistics of the imaging data, and the column mean of the corrected image is too smooth, which can achieve better correction effect for remote sensing images with relatively uniform scenes.…”

Section: Introductionmentioning

confidence: 99%

Non-uniformity Correction Method of Remote Sensing Images based on Adaptive Moving Window Moment Matching

Yan¹,

Kang²,

Ni³

et al. 2022

jist

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To improve the quality of optical remote sensing images, this paper proposes an adaptive moving window moment matching method for non-uniformity correction of remote sensing images. First, the scene complexity of the remote sensing image to be corrected is calculated, and the range of the width of the moving window is determined accordingly; Subsequently, the local scene complexity of the image in the window centered on the column to be corrected is calculated, and the width of the moving window is controlled adaptively; the correction gain and offset values of the column to be corrected of the image in the moving window are then calculated, and the correction of the columns to be corrected is performed accordingly; finally, the whole remote sensing image is traversed, and moment matching correction is performed column by column, and the correction of the non-uniformity of the whole remote sensing image is completed. The experimental results show that the non-uniformity correction effect of this algorithm is better than other competing algorithms, and the mean value of non-uniformity coefficient of the corrected image is reduced by more than 0.1802% compared with the minimum value in other comparison algorithms, which effectively eliminates the streak and stripe noise in the image, and better preserves the detailed information of the image.

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