Image acquisition is usually vulnerable to bad weathers, like haze, fog and smoke. Haze removal, namely dehazing has always been a great challenge in many fields. This paper proposes an efficient and fast dehazing algorithm for addressing transmission map misestimation and oversaturation commonly happening in dehazing. We discover that the transmission map is commonly misestimated around the edges where grayscale change abruptly. These Transmission MisEstimated (TME) edges further result in halo artifacts in patch-wise dehazing. Although pixel-wise method is free from halo artifacts, it has trouble with oversaturation. Therefore, we firstly propose a TME recognition method to distinguish TME and non-TME regions. Secondly, we propose a Multi-scale Optimal Fusion (MOF) model to fuse pixel-wise and patch-wise transmission maps optimally to avoid misestimated transmission region. This MOF is then embedded into patch-wise dehazing to suppress halo artifacts. Furthermore, we provide two post-processing methods to improve robustness and reduce computational complexity of the MOF. Extensive experimental results demonstrate that, the MOF can achieve additional improvement beyond the prototypes of the benchmarks; in addition, the MOF embedded dehazing algorithm outperforms most of the state-of-the-arts in single image dehazing. For implementation details, source code can be accessed via https://github.com/phoenixtreesky7/mof_dehazing .

Multi-scale Optimal Fusion model for single image dehazing

Recently, the method based on fractional order partial differential equation has been used in image processing. Usually, the optional order of fractional differentiation is determined by a lot of experiments. In this paper, a denoising model is proposed based on adaptive fractional order anisotropic diffusion. In the proposed model, the complexity of the local image texture is reflected by the local variance, and the order of the fractional differentiation is determined adaptively. In the process of the adaptive fractional order model, the discrete Fourier transform is applied to compute the fractional order difference as well as the dynamic evolution process. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of the proposed image denoising algorithm is better than that of other some algorithms. The proposed algorithm not only can keep the detailed image information and edge information, but also obtain a good visual effect.

Image Denoising Based on Adaptive Fractional Order Anisotropic Diffusion

This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs.

/pdf/modeling-punching-shear-capacity-of-fiber-reinforced-polymer-4twwr8325z.pdf

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image enhancement algorithms, thus facilitating easier determination of interesting solar burst activities from recorded images/movies.

/pdf/guided-filtering-for-solar-image-video-processing-2es6yt8ms6.pdf

Guided filtering for solar image/video processing

In this paper, we present a novel and efficient algorithm for image inpainting based on the structure and texture components. In our method, after decomposing the image into its texture and structure components using PCA (Principal Component Analysis), these components are inpainted separately using the proposed algorithm. Finally, the inpainted image is simply acquired by adding the two inpainted images. For structure inpainting we used quadtree concept to identify the importance of each pixel located on the boundary of the target region. Subsequently, we detect the correct path for filling so that this path demonstrates an orientation for the better structure inpainting. It is noteworthy that structure inpainting is more important because human vision is sensitive to the coherence of structure. For texture inpainting, we use Euclidean distance in the texture component for patch selection. Also, the geometric feature is considered by LSK (Local Steering Kernel) in the original image to assist chooing a better patch candidate. The experimental results of our algorithm demonstrate the effectiveness of the proposed method.

https://www.ije.ir/article_107234_9a913e267bea83fcd4b9637bf22856cd.pdf

Improving Image Inpainting based on Structure and Texture Information Using Quadtree

We propose a residual-based method for denoising images corrupted by Gaussian noise. In the method, by combining bilateral filter and structure adaptive kernel filter together with the use of the image residuals, the noise is suppressed efficiently while the fine features, such as edges, of the images are well preserved. Our experimental results show that, in comparison with several traditional filters and state-of-the-art denoising methods, the proposed method can improve the quality of the restored images significantly.

/pdf/a-residual-based-kernel-regression-method-for-image-t7f4srjp68.pdf

A Residual-Based Kernel Regression Method for Image Denoising

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A Residual-Based Kernel Regression Method for Image Denoising