It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortionrecovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.

In this paper, a second-order consensus algorithm with input constraints was introduced. The condition of the algorithm for reaching consensus was given. Via Lyapnov stability criterion, the stability of the algorithm was proved. Based on this algorithm, a new control algorithm for swarm robots formation control was also proposed. Finally, from the simulation results of the above algorithms, it can be found that the algorithms with input constraints showed a better consensus performance than the algorithm without input constraints.

We propose a novel robust method to accurately align large-photometric-variation and noisy images emerged from high dynamic range (HDR) imaging. First, the keypoints are detected from optimal multi-binary images to eliminate the noise and photometric effects. The feature descriptor, which is translation-, rotation- and scale-invariant and robust to noise and photometric changes, is then developed, and feature matching is implemented efficiently by use of the structure information of the keypoints. Finally, mutual information (MI) is employed in the RANSAC method for homography estimation and performance assessment, which makes the results accurate and stable. Experiments carried out on synthesized images demonstrate that the proposed method is much more robust to both photometric changes and noise than the SIFT method, the state-of-the-art alignment method.