Wenda Zhao

Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion

Display Omitted Highly promising and applied noisy multispectral image fusion.Adopting the matrix of structure tensor to fuse the gradient information.Gradient entropy metric-based weighted gradient to extract image features, avoiding noise interference.Local adaptive p-Laplace diffusion constraint is constructed while rebuilding the fused gradient field rebuilding the fused image from the fused gradient field. Noise is easily mistaken as useful features of input images, and therefore, significantly reducing image fusion quality. In this paper, we propose a novel gradient entropy metric and p-Laplace diffusion constraint-based method. Specifically, the method is based on the matrix of structure tensor to fuse the gradient information. To minimize the negative effects of noise on the selections of image features, the gradient entropy metric is proposed to construct the weight for each gradient of input images. Particularly, the local adaptive p-Laplace diffusion constraint is constructed to further suppress noise when rebuilding the fused image from the fused gradient field. Experimental results show that the proposed method effectively preserves edge detail features of multispectral images while suppressing noise, achieving an optimal visual effect and more comprehensive quantitative assessments compared to other existing methods.

Multisensor Image Fusion and Enhancement in Spectral Total Variation Domain

Most existing image fusion methods assume that at least one input image contains high-quality information at any place of an observed scene. Thus, these fusion methods will fail if every input image is degraded. To address this issue, this study proposes a novel fusion framework that integrates image fusion based on spectral total variation (TV) method and image enhancement. For spatially varying multiscale decompositions generated by the spectral TV framework, this study verifies that the decomposition components can be modeled efficiently by tailed

Infrared image detail enhancement based on the gradient field specification

\alpha

Guide filter-based gradient vector flow module for infrared image segmentation.

-stable-based random variable distribution (TRD) rather than the commonly used Gaussian distribution. Consequently, salience and match measures based on TRD are proposed to fuse each sub-band decomposition. The spatial intensity information is also adopted to fuse the remainder of the image decomposition components. A sub-band adaptive gain function family based on TV spectrum and space variation is constructed for fused multiscale decompositions to enhance fused image simultaneously. Finally, numerous experiments with various multisensor image pairs are conducted to evaluate the proposed method. Experimental results show that even if the input images are degraded, the fused image obtained by the proposed method achieves significant improvement in terms of edge details and contrast while extracting the main features of the input images, thereby achieving better performance compared with the state-of-the-art methods.

Medical Image Fusion and Denoising with Alternating Sequential Filter and Adaptive Fractional Order Total Variation

Human vision is sensitive to the changes of local image details, which are actually image gradients. To enhance faint infrared image details, this article proposes a gradient field specification algorithm. First we define the image gradient field and gradient histogram. Then, by analyzing the characteristics of the gradient histogram, we construct a Gaussian function to obtain the gradient histogram specification and therefore obtain the transform gradient field. In addition, subhistogram equalization is proposed based on the histogram equalization to improve the contrast of infrared images. The experimental results show that the algorithm can effectively improve image contrast and enhance weak infrared image details and edges. As a result, it can give qualified image information for different applications of an infrared image. In addition, it can also be applied to enhance other types of images such as visible, medical, and lunar surface.

Variational infrared image enhancement based on adaptive dual-threshold gradient field equalization

Infrared image segmentation is a challenging topic since infrared images are characterized by high noise, low contrast, and weak edges. Active contour models, especially gradient vector flow (GVF), have better segmentation performance for clear images. However, the GVF model has the drawbacks of sensitivity to noise and adaptability of the parameters, decreasing the effect of infrared image segmentation significantly. To address these problems, this paper proposes a guide filter-based gradient vector flow module for infrared image segmentation (GFGVF). First, a guide filter is exploited to construct a novel edge map, providing characteristics of the image edge while excluding the effects of noise. This alleviates the possibility of edge leakage caused by using the traditional edge map. Then, a novel weighting function is constructed to effectively handle the extended capture range and preserving the edge even with noise existing. The experimental results demonstrate that the GFGVF model possesses good properties such as large capture range, concavity convergence, noise robustness, and alleviative boundary leakage.

Local region statistics combining multi-parameter intensity fitting module for medical image segmentation with intensity inhomogeneity and complex composition

Medical image fusion aims at integrating information from multimodality medical images to obtain a more complete and accurate description of the same object, which provides an easy access for image-guided medical diagnostic and treatment. Unfortunately, medical images are often corrupted by noise in acquisition or transmission, and the noise signal is easily mistaken for a useful characterization of the image, making the fusion effect drop significantly. Thus, the existence of noise presents a great challenge for most of traditional image fusion methods. To address this problem, an effective variation model for multimodality medical image fusion and denoising is proposed. First, a multiscale alternating sequential filter is exploited to extract the useful characterizations (e.g., details and edges) from noisy input medical images. Then, a recursive filtering-based weight map is constructed to guide the fusion of main features of input images. Additionally, total variation (TV) constraint is developed by constructing an adaptive fractional order

Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network

p

Local binary pattern metric-based multi-focus image fusion

based on the local contrast of fused image, further effectively suppressing noise while avoiding the staircase effect of the TV. The experimental results indicate that the proposed method performs well with both noisy and normal medical images, outperforming conventional methods in terms of fusion quality and noise reduction.