Pixel-level image fusion

FusionGAN: A generative adversarial network for infrared and visible image fusion

Infrared and visible image fusion methods and applications: A survey

Multi-focus image fusion with a deep convolutional neural network

Infrared and visible image fusion via gradient transfer and total variation minimization

A general framework for image fusion based on multi-scale transform and sparse representation

Multi-focus image fusion with dense SIFT

Chessboard corner detection is a necessary procedure of the popular chessboard pattern-based camera calibration technique, in which the inner corners on a two-dimensional chessboard are employed as calibration markers. In this study, an automatic chessboard corner detection algorithm is presented for camera calibration. In authors' method, an initial corner set is first obtained with an improved Hessian corner detector. Then, a novel strategy that utilises both intensity and geometry characteristics of the chessboard pattern is presented to eliminate fake corners from the initial corner set. After that, a simple yet effective approach is adopted to sort the detected corners into a meaningful order. Finally, the sub-pixel location of each corner is calculated. The proposed algorithm only requires a user input of the chessboard size, while all the other parameters can be adaptively calculated with a statistical approach. The experimental results demonstrate that the proposed method has advantages over the popular OpenCV chessboard corner detection method in terms of detection accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method used for camera calibration is also verified in authors' experiments.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2015.0126

Automatic chessboard corner detection method

In this paper, we present a novel medical image fusion method by taking the complementary advantages of two powerful image representation theories: nonsubsampled contourlet transform (NSCT) and sparse representation (SR). In our fusion algorithm, the NSCT is firstly performed on each of the pre-registered source images to obtain the low-pass and high-pass coefficients. Then, the low-pass bands are merged with a SR-based fusion approach, and the high-pass bands are fused by employing the absolute values of coefficients as activity level measurement. Finally, the fused image is obtained by performing inverse NSCT on the merged coefficients. Several sets of medical source images with different combinations of modalities are used to test the effectiveness of the proposed method. Experimental results demonstrate that our method owns clear advantages over the fusion method based on NSCT or SR individually in terms of both visual quality and objective assessments.

Medical Image Fusion by Combining Nonsubsampled Contourlet Transform and Sparse Representation

Chessboard corner detection is a fundamental work of the popular chessboard pattern-based camera calibration technique. In this paper, a fast and robust algorithm for chessboard corner detection is presented. In our method, an initial corner set is obtained with an improved Hessian corner detector. And then, a novel strategy which takes both textural and geometrical characteristics of a chessboard into consideration is employed to eliminate fake corners in the initial corner set. The proposed algorithm only requires a user-input of the total number of chessboard inner corners, while all the other parameters can be adaptively calculated with a statistical approach. Experimental results on two public data sets demonstrate that the proposed method can outperform the most commonly used OpenCV method in terms of both detection rate and computational efficiency.

Shuping Liu

Papers

A general framework for image fusion based on multi-scale transform and sparse representation

Multi-focus image fusion with dense SIFT

Automatic chessboard corner detection method

Medical Image Fusion by Combining Nonsubsampled Contourlet Transform and Sparse Representation

A practical algorithm for automatic chessboard corner detection