Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. According to the extracted features, the illumination components are estimated by Gaussian low-pass filtering. The illumination components are fused to complete the adaptive enhancement of high-resolution satellite images. Simulation results show that the proposed algorithm has a better adaptive enhancement effect, higher image definition, and shorter enhancement time.

Dynasty recognition algorithm of an adaptive enhancement capsule network for ancient mural images

In view of the polysemy of mural images and the style difference among mural images painted in different dynasties as well as the high energy costs of the traditional manual dynasty classification method, which resorts to mural texts and historical documents, this study proposed an adaptive enhancement capsule network (AECN) for automatic dynasty identification of mural images. Based on the original capsule network, we introduced a preconvolution structure to extract the high-level features of the mural images from Mogao Grottoes, such as color and texture. Then, we added an even activation operation to the layers of the network to enhance the fitting performance of the model. Finally, we performed adaptive modifications on the capsule network to increase the gradient smoothness of the model, based on which to optimize the model and thus to increase its classification precision. With the self-constructed DH1926 data set as the study subject, the proposed model achieved an accuracy of 84.44%, an average precision of 82.36%, an average recall rate of 83.75% and a comprehensive assessment score F1 of 83.96%. Compared with modified convolution neural networks and the original capsule network, the model proposed in study increased all the considered indices by more than 3%. It has a satisfactory fitting performance, which can extract the rich features of mural images at multiple levels and well express their semantic information. Furthermore, it has a higher accuracy and better robustness in the classification of the Mogao Grottoes murals, and therefore is of certain application values and research significance.

Genomic bases underlying the adaptive radiation of core landbirds

Background Core landbirds undergo adaptive radiation with different ecological niches, but the genomic bases that underlie their ecological diversification remain unclear. Results Here we used the genome-wide target enrichment sequencing of the genes related to vision, hearing, language, temperature sensation, beak shape, taste transduction, and carbohydrate, protein and fat digestion and absorption to examine the genomic bases underlying their ecological diversification. Our comparative molecular phyloecological analyses show that different core landbirds present adaptive enhancement in different aspects, and two general patterns emerge. First, all three raptorial birds (Accipitriformes, Strigiformes, and Falconiformes) show a convergent adaptive enhancement for fat digestion and absorption, while non-raptorial birds tend to exhibit a promoted capability for protein and carbohydrate digestion and absorption. Using this as a molecular marker, our results show relatively strong support for the raptorial lifestyle of the common ancestor of core landbirds, consequently suggesting a single origin of raptors, followed by two secondary losses of raptorial lifestyle within core landbirds. In addition to the dietary niche, we find at temporal niche that diurnal birds tend to exhibit an adaptive enhancement in bright-light vision, while nocturnal birds show an increased adaption in dim-light vision, in line with previous findings. Conclusions Our molecular phyloecological study reveals the genome-wide adaptive differentiations underlying the ecological diversification of core landbirds.