When images are captured under hazy conditions, light is attenuated and deflected by particle scattering, resulting in reduced brightness and color distortion, which affects the imaging quality of the visual system. This paper proposes a defogging method that combines super-pixel segmentation and transmission optimization. First, the complexity of the haze image was calculated using color entropy to adaptively determine the number of super-pixel blocks. The simple linear iterative clustering (SLIC) super-pixel segmentation method was used to obtain super-pixel blocks with the same features. And the super-pixel block with the highest score was selected as a candidate block to accurately estimate the atmospheric light value. Then, the transmission was estimated using the multiscale dark channel prior and the non-local haze-lines prior, and then the initial transmission after fusion was obtained by wavelet transform. In addition, a guided filter based on unsharp masking was introduced to further improve the transmission estimation accuracy. Finally, an atmospheric scattering model was used to invert the haze-free image. We have conducted a large number of quantitative and qualitative experiments on three datasets, and the results show that the proposed algorithm can achieve a better de-fogging effect, especially in the sky region, where the image restoration effect is more prominent.

The objective of this study is to address semantic misalignment and insufficient accuracy in edge detail and discrimination detection, which are common issues in deep learning-based change detection methods relying on encoding and decoding frameworks. In response to this, we propose a model called FlowDual-PixelClsObjectMec (FPCNet), which innovatively incorporates dual flow alignment technology in the decoding stage to rectify semantic discrepancies through streamlined feature correction fusion. Furthermore, the model employs an object-level similarity measurement coupled with pixel-level classification in the PixelClsObjectMec (PCOM) module during the final discrimination stage, significantly enhancing edge detail detection and overall accuracy. Experimental evaluations on the change detection dataset (CDD) and building CDD demonstrate superior performance, with F1 scores of 95.1% and 92.8%, respectively. Our findings indicate that the FPCNet outperforms the existing algorithms in stability, robustness, and other key metrics.

In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data, a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed. The algorithm took Kalman filter (KF) as the whole frame, and established the decision threshold based on the confidence level of Chi-square distribution. At the same time, the abnormal error judgment value was constructed by Mahalanobis distance function, and the three segments of Huber weight function were formed. It could improve the accuracy of the interval judgment of outliers, and give a reasonable weight, so as to improve the tracking accuracy of the algorithm. The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion. According to theoretical analysis, compared with Kalman filtering algorithm, the proposed algorithm could accurately track the actual temperature in the case of abnormal error, and multi-station data fusion processing could improve the overall fault tolerance of the system. The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering, and the synthetic value of fusion processing was more stable and critical.