Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideratio...Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models＇ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models.展开更多
We study the problem of low lighting image en- hancement. Previous enhancement methods for images un- der low lighting conditions usually fail to consider the factor of image degradation during image formation. As a r...We study the problem of low lighting image en- hancement. Previous enhancement methods for images un- der low lighting conditions usually fail to consider the factor of image degradation during image formation. As a result, the lost contrast could not be recovered after enhancement. This paper will adaptively recover the contrast and adjust the exposure for low lighting images. Our first contribution is the modeling of image degradation in low lighting con- ditions. Then, the local maximum color value prior is pro- posed, i.e., in most regions of well exposed images, the lo- cal maximum color value of a pixel will be very high. By combining the image degradation model and local maximum color value prior, we propose to recover the un-degraded im- ages under low lighting conditions. Last, an adaptive expo- sure adjustment module is proposed to obtain the final result. We show that our approach enables better enhancement com- paring with popular image editing tools and academic algo- rithms.展开更多
文摘Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models＇ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models.
文摘We study the problem of low lighting image en- hancement. Previous enhancement methods for images un- der low lighting conditions usually fail to consider the factor of image degradation during image formation. As a result, the lost contrast could not be recovered after enhancement. This paper will adaptively recover the contrast and adjust the exposure for low lighting images. Our first contribution is the modeling of image degradation in low lighting con- ditions. Then, the local maximum color value prior is pro- posed, i.e., in most regions of well exposed images, the lo- cal maximum color value of a pixel will be very high. By combining the image degradation model and local maximum color value prior, we propose to recover the un-degraded im- ages under low lighting conditions. Last, an adaptive expo- sure adjustment module is proposed to obtain the final result. We show that our approach enables better enhancement com- paring with popular image editing tools and academic algo- rithms.
