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Convolutional Neural Networks (CNNs) have played an important role in saliency detection. How to detect a salient object as a whole is a key issue. However, most existing learning-based methods are not accurate enough to detect salient objects in complex scenes, such as easily overlooked small salient areas in a whole salient object, which is called scale imbalance problem in this paper. To address this issue, Scale Balance Network (SBN) based on fully convolutional network is proposed to accurately recognize and comprehensively detect salient objects. Firstly, to detect more small salient areas, a specially designed backbone instead of common backbone is adopted in this paper, which can capture larger resolution with more spatial features in deeper layers. Secondly, we present a novel progressive pyramid mechanism named Connective Feature Pyramid Module (CFPM), aiming to make the network focus on the balance between the large salient areas and the small ones. Finally, we present an Edge Enhancement Architecture with Various Kernels (EEAVK) to locate the saliency maps and refine the boundary features. Experimental results on five benchmark datasets show that the proposed SBN method achieves consistently superior performance in comparison with other state-of-the-art ones under different evaluation metrics.

Color transfer in image processing usually suffers from misleading color mapping and loss of details. This paper presents a novel directive local color transfer method based on dynamic look-up table (D-DLT) to solve these problems in two steps. First, a directive mapping between the source and the reference image is established based on the salient detection and the color clusters to obtain directive color transfer intention. Then, dynamic look-up tables are created according to the color clusters to preserve the details, which can suppress pseudo contours and avoid detail loss. Subjective and objective assessments are presented to verify the feasibility and the availability of the proposed approach. Experimental results demonstrate that our proposed method has better performance on natural color images than classical color transfer algorithms. Furthermore, the reference image can be extended to color blocks instead of images.

Color transfer is to change the image color according to one or more reference images to match people’s cognition. Usually, there are two problems deserved to be researched further. One is to find suitable reference colors for a complex image. The other is to lessen the details loss in the process. In the paper, GMM model is improved by considering the geometric position relation of the color clustering to construct color transfer intentions between source image and multiple reference images. The geometric coordinate smoothing parameter is introduced to improve EM algorithm to reduce the details loss. Based on Baidu data set and the reference paper data sets, a comparison experiment between typical color transfer algorithms and our method was presented. The experimental results can demonstrate that the algorithm proposed in the paper is reasonable and effective.