To remove noise from infrared thermal images captured in underground mining working face under low luminance and dusty environment, a nonreference infrared thermal image denoising method based on heuristic dual-tree wavelet thresholding is proposed. The threshold is optimized through estimating noise variance in wavelet domain using an improved chaotic drosophila algorithm (CDOA), which is promoted by a spatial–spectral entropy based metric. The basic DOA, genetic algorithm, particle swarm optimization algorithm, and virus colony search algorithm are implemented to compare the convergence rate and optimization ability of improved CDOA. Moreover, other representative denoising methods, such as BM3D, BLS-GSM, fast translation invariant, and nonlocal Bayes, are also applied for comparison. Comparison result proves effectiveness and superiority of the proposed method. Finally, the proposed method is applied in infrared thermograph-based visual surveillance system, and the denoising results also prove the state-of-art performance.
In this paper, a nonlinear least square twin support vector machine (NLSTSVM) with the integration of active contour model (ACM) is proposed for noisy image segmentation. Efforts have been made to seek the kernel-generated surfaces instead of hyper-planes for the pixels belonging to the foreground and background, respectively, using the kernel trick to enhance the performance. The concurrent self organizing maps (SOMs) are applied to approximate the intensity distributions in a supervised way, so as to establish the original training sets for the NLSTSVM. Further, the two sets are updated by adding the global region average intensities at each iteration. Moreover, a local variable regional term rather than edge stop function is adopted in the energy function to ameliorate the noise robustness. Experiment results demonstrate that our model holds the higher segmentation accuracy and more noise robustness.
Compared with gray images, colorful images contain more useful information. In this paper, an online active contour model regarding color image matting is proposed. Using our model, the objects of color images are detected according to their colors. For the proposed model, the new scheme firstly identifies the objects to be segmented by setting the initial contour. Then the new energy functional, which is based on the intensities in each channel, is minimized through an efficient level set formula. Thus less iterations and little calculation time are needed. Finally, the morphological opening and closing operation is adopted for regularization. Experiments results demonstrate the efficiency and effectiveness of the proposed approach, compared with the current active contour models.