Restoration algorithm for noisy complex illumination

Abstract

Although promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi-scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double-density dual-tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state-of-the-art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises.

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“…21 MSRCR is often used in the restoration of complex illumination images. 22 First step of MSRCR calculation is calculated the enhanced color value R i (x, y), as shown in (3). I i (x, y) is the original color value of the point with pixel coordinates (x, y) on the color channel i, F k (x, y)) is the Gaussian wrap, and its calculation is shown in (4), C k is the scale value of Gaussian wrap, it means the neighborhood size of (x, y) during convolution operation.…”

End‐to‐end autonomous driving decision model joined by attention mechanism and spatiotemporal features

“…However, rulebased decision methods often need to build a huge rule base, which is complex in calculation and high in operation cost [6]. With the rapid development of deep learning, the famous deep neural networks such as VGGNet [7], GoogleNet [8] and ResNet [9] were proposed and widely used [10][11][12][13]. Nvidia…”

“…However, rulebased decision methods often need to build a huge rule base, which is complex in calculation and high in operation cost [6]. With the rapid development of deep learning, the famous deep neural networks such as VGGNet [7], GoogleNet [8] and ResNet [9] were proposed and widely used [10][11][12][13]. Nvidia [14,15] propose an end-to-end convolution neural network decision system PilotNet that regress steering angles directly from raw pixels recorded by front-view cameras, which improves the flexibility of unmanned driving decision and simplifies the decision-making process.…”