Brain Medical Image Fusion Using <i>L2</i>-Norm-Based Features and Fuzzy-Weighted Measurements in 2-D Littlewood–Paley EWT Domain

Jin, Xin; Jiang, Qian; Chu, Xing; Lang, Xun; Yao, Shaowen; Li, Keqin; Zhou, Wei

doi:10.1109/tim.2019.2962849

Cited by 43 publications

(11 citation statements)

References 70 publications

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“…Image colors are restored by controlling saturation, and image contrast between different channels is also improved. Image fusion is an important method used in image defogging, which can effectively improve the image contrast, detail information and so on (Jin et al, 2020 ; Liu et al, 2020 ). In the same scene, since the imaging equipment cannot focus different depth objects at the same time, so multi-focus image fusion technology is used to extract different focus areas from multiple images to synthesize a clear image (Jin et al, 2018b ; Liu et al, 2019b ).…”

Section: Related Workmentioning

confidence: 99%

Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Liu

Luo

et al. 2021

Front. Neurorobot.

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Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Liu

Luo

et al. 2021

Front. Neurorobot.

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“…Based on the two assumptions that reflectance is a piece-wise constant and illumination is spatially smooth, the second term proposed represents the spatial regularization term of the reflectance component R, and the third term represents the spatial regularization term of the illumination component L. The second and third terms reflect a priori information about the image to be reconstructed. According to some existing algorithms in low-light image enhancement based on the Retinex variational model, L 1 and L 2 norms [22][23][24] etc. can be used to constrain the illumination and reflectance components for more effective estimation.…”

Section: Retinex Modelmentioning

confidence: 99%

Low‐light image enhancement based on exponential Retinex variational model

Chen

Hua

2021

IET Image Processing

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Aiming at the problems of residual noise, low contrast, and limited detail information caused by low-light images, this paper proposes a new Retinex variational model. According to Retinex theory, it is necessary to estimate the illumination and reflectance components decomposed from the original image. In order to better maintain the edge information, texture richness, and prevent artefacts, the exponential forms of local variation deviation and total variation are used as illumination prior and reflectance prior, respectively, and mixed norms are used to constrain them, so as to deal with the illumination information and texture details of the image more effectively, and then use the bright channel prior to improve the colour reproduction sense of the original image, thereby constructing the objective function, and finally using the alternating iterative optimization method to find the optimal solution to the proposed model. Experiments show that compared with other existing image enhancement methods, the method proposed here improves the contrast of the image, overcomes the phenomenon of halo artefacts and colour distortion, is more consistent with human vision, and produces better results in terms of quantitative performance.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Low informativeness of 2D and 3D images often does not contain enough information for highquality diagnosis is one of them. Multiple views combining the same organ solve this problem in practice [6,7]. The resulting image is more informative and facilitates perception by both humans and machines, increasing diagnostics' accuracy.…”

Section: Introductionmentioning

confidence: 99%

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

et al. 2022

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Medical imaging using different modalities has many problems. The main ones are low informativeness, various distortion noises, and a large amount of information. Fusion, denoising, and visual data compression are used to solve them in practice. Discrete wavelet transform is one way to implement various fusion, denoising, and compression methods for 2D and 3D medical image processing. Medical imaging systems produce increasingly accurate images with scanning technology and digital devices development. These images have improved quality using both higher spatial resolutions and color bit-depth. Processing a large volume of medical imaging data requires considerable resources and processing time. Modern wavelet-based devices for medical image processing do not meet the current performance demand. Hardware accelerators are being designed to solve this problem. This paper proposes new (fieldprogrammable gate array) FPGA accelerators using wavelet processing (WP) with scaled filter coefficients (SFC) and parallel computing in residue number system (RNS) to improve the performance of high-quality 3D medical image WP systems. The computational complexity is reduced using the developed WP method with SFC and the proposed wavelet filter coefficients scaling algorithm. Parallel computing is organized in RNS using moduli sets of a particular type. Hardware implementation of 3D medical image WP using the proposed FPGA accelerators increases device performance by 2.89-3.59 times, increasing the hardware resources by 1.18-3.29 times compared to state-of-the-art solutions. The device performance improvement is achieved while maintaining high-quality 3D medical image processing in peak signal-to-noise ratio terms.INDEX TERMS Medical image processing, discrete wavelet transform, scaled filter coefficients, residue number system, high-performance computing, hardware accelerator, field-programmable gate arrays.

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Brain Medical Image Fusion Using L2-Norm-Based Features and Fuzzy-Weighted Measurements in 2-D Littlewood–Paley EWT Domain

Cited by 43 publications

References 70 publications

Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Low‐light image enhancement based on exponential Retinex variational model

RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

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