An improved non-local means filter algorithm is proposed. The common NLM algorithm only considers the Euclidean distance between pixel values as the calculation standard of weights, neglects the spatial position relationship of pixels and the similarity of texture details between image blocks, which results in the distortion of image structure after filtering, and the edge information is missing. To solve this problem, the author uses the spatial position of pixels in the image to improve the Euclidean distance. At the same time, the structural similarity index measurement (SSIM) is used to measure the similarity of neighbourhood image blocks to obtain the similarity weight, using this weight, the Euclidean distance of the image block is weighted again to reduce the weight of image blocks with low structural similarity. At the same time, the weight of the image blocks with high structural similarity is increased to achieve the ability to maintain the edge information. The experimental results show that the proposed algorithm effectively maintains the edge and detail of the image, and is superior to the conventional NLM algorithm in terms of PSNR and SSIM indicators.
When the traditional extreme learning machine is dealing with unbalanced data sets, the classification effect of the small number of samples is not ideal. A weighted extreme learning machine based on KFCM is proposed for this problem, and different penalty factors are given according to the proportion of samples in different categories.At the same time, considering the impact of outliers, the KFCM clustering gets the degree of membership that each type of sample belongs to, and adopts the degree of membership to conduct quadratic weighted means on penalty factors of extreme learning machine. Due to the high cost of calculating the generalized inverse of the weighted extreme learning machine, a method of cholesky decomposition is proposed. The simulation test results of the UCI standard datasets show that the proposed algorithm not only effectively improves the classification accuracy of the minority samples, but also achieves the optimal performance in the F-measure and G-means indexes, and the computation speed is much faster than the ordinary extreme learning machine algorithm.
Proc. SPIE. 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence
KEYWORDS: Thermography, Signal to noise ratio, Infrared imaging, Detection and tracking algorithms, Video, Field programmable gate arrays, Infrared radiation, Image enhancement, Video processing, Reconstruction algorithms
Due to that the infrared thermal imaging system has the characteristics of low contrast and small dynamic range, this paper proposed an real-time infrared image enhancement algorithm based on Limit Contrast Adaptive Histogram Equalization (CLAHE) and also provided the algorithm implementation. The algorithm firstly divides the pretreated image data into several sub-regions of size, and then the histogram of the sub-region is calculated respectively, the clipping threshold of histogram is determined according to the image gradient information, the captured pixels are evenly distributed to each gray level. Finally, bi-linear interpolation is used to remove the unbalance effect of block edge transition. Experimental results show that compared with traditional algorithms, this algorithm is capable of suppressing the noise and highlight the edges and details of the image, as well as meeting the real-time requirement.
Traditional face recognition based on the machine learning often adopts the batch learning method, but in the practical applications, the training data of face recognition system can not be obtained at one time, but is obtained one by one with the passage of time. When there are new training samples, the whole system needs to be retrained by using batch learning method. In order to solve this problem, an incremental learning algorithm, online sequential extreme learning machine, is applied to the face recognition. The algorithm can not only train the data one after another, but also can be learned from one batch after another. Experimental results show that this algorithm has the advantages of high speed, high recognition rate and simple parameter selection in the face recognition, and it can be used as a good choice for the online updating of the face recognition system.
An infrared image enhancement algorithm combining dark channel prior and adaptive limited contrast enhancement is proposed. Firstly, using the physical model of infrared atmospheric transmission and combining the principle of dark primary color enhancement, the infrared image before atmospheric transmission degradation is restored. Afterwards, the enhanced image is divided into basic sub-graphs and detailed sub-graphs by using the method of guided filtering. The basic sub-graph is further adjusted by Limit contrast histogram enhancement (CLAHE) algorithm, and the detailed sub-graph is processed by gamma transform after being filtered by non-local mean filtering. Finally, wavelet transform is used to fuse the two enhanced sub-graphs. The experimental results show that the algorithm can effectively improve the contrast of the image and make the details of the image highlight.
Batch learning method is usually adopted for traditional SAR target identification, but training data of a system cannot be completely acquired at one time in practical application. When a new training sample is added, the batch training method needs to retrain the whole system. In order to solve this problem, cholesky factorization principle was adopted in this paper to promote extreme learning machine to an incremental learning form and apply it in the classifier training for SAR target identification. Moreover, in allusion to disadvantageous approximation capability of traditional single kernel function, a multi-scale wavelet kernel function was established to improve classification performance thereof. Experiment results show: when new SAR target sample is obtained, this algorithm only needs to update output weight value to update the system, without any retraining; it has extremely fast speed, with identification rate higher than that of traditional kernel extreme learning machine, SVM algorithm, etc., thus becoming a good choice for the online updating of SAR target identification system.