We present an effective blind image deblurring algorithm based on superpixel segmentation prior (SSP). The motivation of this work is an interesting observation that the more rough the segmentation boundaries are, the clearer the image will be. Intuitively, the blurry images have less image details, which results in more smooth segmentation boundaries. The clear images have more vivid textural details and obtain more rough boundaries. The segmentation roughness could be defined as the length of image segmentation boundaries. However, the segmentation boundary length is not differentiable, making it difficult to integrate into existing joint optimization framework. Therefore, we transform the segmentation boundary length into the segmentation entropy to guide the process of image deblurring. With the image becomes clearer, its boundary becomes more rough, while the segmentation entropy is much smaller. The analysis of relationship between segmentation entropy and segmentation boundaries is detailed. Benefiting from the convexity of segmentation entropy, we propose a novel algorithm by integrating half-quadratic split and gradient descent to alternately minimize energy function. Extensive experiments show that the proposed method achieves best performance with the state-of-the-art blind deblurring methods on natural and face image deblurring.

Existing unsupervised video object segmentation generates object information from the whole video, which ignores analysis of the local clips. However, we observe that local clips and their relationships are also useful for the video object segmentation. For example, the simple background clips can be used to improve the segmentation of complex background clips. In this paper, we propose a novel unsupervised segmentation framework to segment the primary object based on two aspects, i.e., the complexity awareness of video clips and their segmentation propagation. The first one is used to select the simple clips with smooth backgrounds and the second one generates an object prior from the simple clips and propagates the object prior to help and improve the segmentation of the complex clips. A complexity awareness method using the static cues and the dynamic cues are proposed to evaluate the complexity of the video frames. A new object prior learning model based on the local part structure is designed and a local part-based prior propagation is proposed for the complex clip segmentation. To verify our method, we collect a new challenging video segmentation data set, in which each video contains diverse backgrounds. Experimental results demonstrate that our method outperforms several state-of-the-art methods both on a classical data set and our new data set.

Fish swarm image segmentation provides an easy to understand and analyze representation for behavior monitoring feature extraction and image information analysis, accurate and effective image segmentation is the basis of fish shoal monitoring. In this paper, algorithm of adaptive fast clustering for fish swarm color image segmentation was proposed by combining the fish swarm hypoxia image features and K-Means++ algorithm. In the RGB color space, the color information of the channel with the maximum average brightness is retained, the others compensation was zero, the generated new image replaced the original. The fish color library was constructed using the gray distribution statistics of the fish swarm targets and background. The pixel probability distribution value in each gray scale range of the normalized gray histogram of the newly generated image is calculated, and combine with the fish group target gray scale reference statistic, the clustering value is determined by two traversing. According to the reserved channel information, the corresponding cluster fish swarm color library is selected for color clustering. The clustering result is processed by threshold transformation to finally realize fish swarm image segmentation. Experiment showed that the average range of structural similarity of our algorithm was [0.93, 1], and the average range of peak signal-to-noise ratio was [44, 50], the running time of the algorithm in this paper is 56% shorter than K-Means ++ algorithm and 71% shorter than the fuzzy clustering algorithm when processing the same images, which could met the requirements of image segmentation quality and accuracy for fish behavior detection.

Most existing image deblurring methods construct statistical prior to describe the difference between blur and clear image. They discard the position information and ignore pixel feature changing in deblurring, which results in inferior restoration performance for images unsatisfying corresponding assumptions. Intuitively, fuzziness of pixel belonging to different image regions will reduce along with image deblurring. This phenomenon could intrinsically describe the pixel characteristic. To this end, we analyze fuzziness of pixels and objects in a blurry image, and utilize the similarity between two fuzzy objects on image pixels to depict the blur degree of an image, which is inspired by overlap functions and overlap indices. To minimize the similarity between fuzzy objects, we introduce the non-parameters model to construct an integer programming problem. Energy minimization could significantly reduce the similarity between two fuzzy objects. Experimental results show that the proposed method can achieve better performance than the state-of-the-art blind deblurring methods on benchmark datasets and natural images.

In this paper, we propose a particle-filter-based superpixel (PFS) segmentation method that extends the original tracking problem as a region clustering problem. The basic idea is to approximate superpixel centers by multiple particles to obtain high intra-region similarity. Specifically, we firstly use a density cluster to initialize single-group particles and introduce the association rule for mining other initial candidate particles. In propagation, particles are transferred to neighboring local regions by a moving step aiming to update local candidate particles with a lower energy cost. We evaluate all particles on the basis of their cluster similarity and estimate the largest particles as the final superpixel centers. The proposed method can locate cluster centers in diverse feature space, which alleviates the risk of a local optimum and produces better segmentation performance. Experimental results on the Berkeley segmentation 500 dataset (BSD500) demonstrate that our method outperforms seven state-of-the-art methods.