Detecting pupil from the image is critical in human-machine interaction and biomedical computing applications, which is supposed to be an actual image segmentation problem. Recently developed deep learning models provide a variety of novel approaches to the pupil segmentation task. However, dataset preparation and annotation acquirement to build pupil image datasets are labor-intensive and time-consuming. The shortage of labeled samples restricted the improvement of deep learning models. In this work, we use a mask image modeling mechanism to learn the latent representation from limited data samples, which significantly helps train deep models. Further, we propose a novel pupil segmentation model based on the recently proposed Swin-Transformer to validate the improvement validity of the mask mechanism. The proposed computational framework achieves better performance on the pupil segmentation tasks based on the LPW dataset through comparison experiments with other related deep learning models. The proposed framework is a promising solution for pupil segmentation and detection in small-sample learning applications.
There are many causes of image blurring, including optical, atmospheric, artificial and technical factors, etc. It is important to deblur images in daily production life. To achieve better results, different methods are needed to deal with different causes of blurring. From the technical aspect, blurred image processing methods are divided into three main categories, namely image enhancement, image restoration and super-resolution reconstruction. Overall, although blurred image processing algorithms have achieved a very wide range of applications, but image algorithms have their own limitations after all, we can not pin all problems on the image algorithm, for different kinds of blurred problems, to be treated differently. For blurring or image quality degradation caused by lens out of focus, dust obscuring, line aging, camera failure, etc., with the help of video diagnostic system, must be repaired in time to solve the problem at the source. For low light and other priority to choose day and night type high-sensitivity cameras, for rain and fog, motion and pre-sampling caused by image quality degradation, you can use the "video enhancement server" contains a variety of blur image processing algorithms to improve image quality. The purpose of single-image deblurring is to restore blurry images to sharp images and recover the texture detail features of the image. The Transformer model is able to capture the correlation of the blurry pixels over long distances, which has significant performance on image deblurring tasks, but its computational complexity grows quadratically with increasing spatial resolution. In addition, some methods use convolution to reduce image resolution, but it leads to information loss. In this paper, we propose an effective Transformer model, named Fourier Unit Transformer (FUformer), based on Fourier transform constructing the shallow features extraction module and the self-attention module, which can reduce computational complexity and fuse image local and global features to recover image texture details. Experimental results show that the proposed method performs well on the deblurring task. Code will be available at https://github.com/zox-iii/FUformer.
Image dehazing can make images clearer and more realistic. The dehaze image is more easily recognized and understood by the human eye, and also helps to improve the performance of the computer vision system. Transformer has found significant application in image dehazing because of its remarkable ability to extract global features. However, employing Transformer solely in image dehazing often leads to the loss of image details, while CNN is ineffective in global feature extraction. Therefore, we integrated CNN and Transformer to enhance image dehazing. We introduced a novel technique to address the feature fusion dilemma by utilizing a transmission-guided map to extract haze-based photo characteristics, thereby uniting the features of CNN and Transformer. The combined features contain both image nuances and extensive-range data. Comprehensive tests indicate that our approach outperforms all others on numerous quality assessment metrics.
Many modern hotels have multiple functions such as accommodation, catering, entertainment, conferences, exhibitions, etc., and consume a lot of energy. At present, most of the predictions of building energy consumption are predictions of building cooling and heating loads and lighting energy consumption. The consideration is lack of building cooling (heating) area ratios, building cooling (heating) methods, meteorological parameters, and personnel activity patterns for large public buildings. By analyzing the data mining status of hotel energy consumption, sorting out concepts and technical support conditions, etc., the article has established a more comprehensive energy monitoring, forecasting, and evaluation system, which has enriched the research scope of the current hotel energy management research system, and has a strong Theoretical significance.
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