Co-Saliency Detection for RGBD Images Based on Multi-Constraint Feature Matching and Cross Label Propagation.

Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely cross label propagation, is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model.

Textured Image Demoiréing via Signal Decomposition and Guided Filtering.

Moiré artifacts are generally caused by the interference between the overlap of the sensor's sampling grid and high-frequency (nearly) periodic textures, and heavily affect the image quality. However, it is difficult to effectively remove moiré artifacts from textured images as the structure of moiré patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoiréing method by signal decomposition and guided filtering. Given a textured image with moiré artifacts, we first remove moiré artifacts in the green (G) channel using the proposed low-rank and sparse matrix decomposition model. This model regularizes the texture layer by the low-rank prior in spatial domain and the moiré layer by sparse representation in frequency domain. An alternating direction method under the augmented Lagrangian multiplier framework is used to solve the matrix decomposition model. Then, since the red (R) and blue (B) channels are more heavily polluted by moiré artifacts than the G channel, we propose to remove moiré artifacts in its R and B channels via guided filtering by the obtained texture layer of the G channel. Experimental results demonstrate that our method outperforms the state-of-the-art methods for both synthetic and real images.

Contrast Enhancement Based on Intrinsic Image Decomposition.

In this paper, we propose to introduce intrinsic image decomposition priors into decomposition models for contrast enhancement. Since image decomposition is a highly illposed problem, we introduce constraints on both reflectance and illumination layers to yield a highly reliable solution. We regularize the reflectance layer to be piecewise constant by introducing a weighted ℓ1 norm constraint on neighboring pixels according to the color similarity, so that the decomposed reflectance would not be affected much by the illumination information. The illumination layer is regularized by a piecewise smoothness constraint. The proposed model is effectively solved by the Split Bregman algorithm. Then, by adjusting the illumination layer, we obtain the enhancement result. To avoid potential color artifacts introduced by illumination adjusting and reduce computing complexity, the proposed decomposition model is performed on the value channel in HSV space. Experiment results demonstrate that the proposed method performs well for a wide variety of images, and achieves better or comparable subjective and objective quality compared with the state-of-the-art methods.