Fusion of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion

Tan, Wei; Zhou, Huixin; Rong, Shenghui; Qian, Kun; Yu, Yue

doi:10.1364/ao.57.010092

Cited by 25 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the adjustment process, the initial values of all images to be equalized are set firstly: 0 a = , 1 b = . The observation equation is established according to formula (12) and the parameter c is calculated by the least square principle;…”

Section: Adjustment Solutionmentioning

confidence: 99%

“…The equation is established for adjustment, and the initial values are set for all images to be equalized: 0 a = , 1 b = . The observation equation is established according to formula (12). The parameters c , b , a are solved in turn by the least square principle, and a certain threshold is set for iterative calculation.…”

Section: Analysis Of Real Experiments (1) Experimental Datamentioning

confidence: 99%

“…This approach adopts the basic assumptions of these two methods, measures color components in image, and selects appropriate routine and arguments to implement auto white balance. Tan et al [12] proposed the fusion method of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion. Ma et al [13] adopted robust L2E estimation of transformation for non-rigid registration.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Aerial Image Color Balancing Based on Rank-Deficient Free Network

et al. 2023

View full text Add to dashboard Cite

In the process of digital image acquisition, color inconsistency is a common issue due to the influence of photographic conditions. The inconsistency of color affects the quality of digital orthophoto products and subsequent applications significantly, and this issue remains challenging both in the academia and industry. This paper proposes a color equalization method for aerial image based on block adjustment and an improved color balance model based on the analysis of causes for color deviation. Two nonlinear functions are used to simulate the process of color deviation and correct the color of images. Due to the existence of error, the color corrected images still have minor deviations in the color of the overlapped regions. Therefore, this research introduces an error equation to address this issue and uses the least square principle to calculate the adjustment. The simulation experiments and real experiments are carried out on several groups of aerial image data respectively. The results show that the method not only achieves better visual effect but also has a higher adjustment precision. Finally, the rationality and parameters of the model proposed in this paper are discussed.

show abstract

Section: Adjustment Solutionmentioning

confidence: 99%

Section: Analysis Of Real Experiments (1) Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aerial Image Color Balancing Based on Rank-Deficient Free Network

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The MST methods mainly contain the Laplacian pyramid (LP) [6], the wavelet transform (WT) [27,34], the non-subsampled contourlet transform (NSCT) [49], and the non-subsampled shearlet transform (NSST) [4,23,38]. However, if the MST method performs without other fusion measures, some unexpected block effect may appear [39].…”

Section: Current Challenges In Multimodal Image Fusionmentioning

confidence: 99%

Multimodal medical image fusion algorithm in the era of big data

Tan

Tiwari

Pandey

et al. 2020

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer's, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.

show abstract

“…So the appropriate method for IR image denoising needs to be explored in the IR image super resolution procession without priors, such as [30]. Subsequently, the curvature filter [31] and guided filter methods [15,32], which can obtain well edge-preserving smoothing and less noise infrared images, are adopted for denoising. With a color image used as the guidance, the IR image can be well reconstructed, and the depth image edge can be preserved completely [14].…”

Section: Ir Image Denoisingmentioning

confidence: 99%

RGB-IR Cross Input and Sub-Pixel Upsampling Network for Infrared Image Super-Resolution

Zhou

Qian

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Deep learning-based image super-resolution has shown significantly good performance in improving image quality. In this paper, the RGB-IR cross input and sub-pixel upsampling network is proposed to increase the spatial resolution of an Infrared (IR) image by combining it with a color image of higher spatial resolution obtained with a different imaging modality. Specifically, this is accomplished by fusion of the features map of two RGB-IR inputs in the reconstruction of an infrared image. To improve the accuracy of feature extraction, deconvolution is replaced by sub-pixel convolution to upsample image in the network. Then, the guided filter layer is introduced for image denoising of IR images, and it can preserve the image detail. In addition, the experimental dataset, which is collected by us, contains large numbers of RGB images and corresponding IR images with the same scene. Experimental results on our dataset and other datasets demonstrate that the method is superior to existing methods in accuracy and visual improvement.

show abstract

Fusion of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion

Cited by 25 publications

References 41 publications

Aerial Image Color Balancing Based on Rank-Deficient Free Network

Aerial Image Color Balancing Based on Rank-Deficient Free Network

Multimodal medical image fusion algorithm in the era of big data

RGB-IR Cross Input and Sub-Pixel Upsampling Network for Infrared Image Super-Resolution

Contact Info

Product

Resources

About