关键词: 目标显著性检测, 全频域显著性检测算法, 对比度拉伸算法, Robert交叉梯度锐化, 背景修正方法

Abstract: For the significant detection algorithms that existed at this stage, the contrast is not clear, the image details of the significant images are not obvious enough, and the background suppression is not complete, this paper proposes a full-frequency saliency detection CRT algorithm based on contrast stretching algorithm and Laplace algorithm to improve Robert cross gradient sharpening. This method uses FT algorithm to calculate its own fast speed and has the advantage of de-drying effect. Contrast stretch enhances the contrast of the significant image and the features of the Robert gradient sharpening algorithm to improve the edge detail and the background correction algorithm proposed in this paper can further suppress the background. It finally gets better saliency map with higher contrast, more obvious detail, and background suppression. The algorithm uses the MSRA10K and THUR15K data sets for significant detection testing, compared with the other six popular saliency detection methods. From the subjective point of view, the contrast of the significant image is obtained, and the image details are superior to the six algorithms compared with it. The objective indicators show that the MAE value obtained by the MSRA10K data set for the significance detection is 0.12, and the MAE value obtained on the THUR15K data set is 0.06, which is better than the six algorithms compared with it. The average structural index S-measure value is 0.8, which is only slightly lower than the HC algorithm, which is better than the other five algorithms. That is to say, the saliency map obtained by the algorithm has the advantages of contrast and image detail enhancement, and has better background suppression effect.

Key words: significant detection algorithmst, full-frequency significance detection algorithm, contrast stretch algorithm, Robert cross gradient sharpening, background suppression effect