Cell-phone use while driving results in potentially severe safety hazards. In this paper, a scheme for detecting cell-phone use that is based on deep learning is proposed, which can eliminate the potential risk by detecting the driver behavior and issuing an early warning. The proposed scheme consists of two stages: model training and practical testing. In the former, a multi-angle arrangement of cameras is first designed. Then, based on self-established data set, two independent convolutional neural networks (CNNs) are trained by optimizing the size and number of the convolution kernels, which can efficiently recognize cell-phones and hands in real time. In the testing stage, dynamic region extraction and skin color detection are employed as preprocessing to improve the accuracy of target recognition. Then, with the trained CNNs, the detection of cell-phone and hand targets is carried out, and the corresponding early warning is issued based on the distance of the interaction between the cell-phone and the hand. Numerous experiments are conducted and the results demonstrate that the proposed scheme can accurately detect cell-phone use during driving in real time, with a running time of 144 fps and an accuracy of 95.7%.

Screen content images (SCIs) have been rapidly and widely applied in interactive multimedia applications. The problem of quality assessment for SCIs is an interesting research topic. Most of the existing methods use subjective and independent features in gray domain to predict the image quality, which can't comprehensively characterize the image properties or lack unified mathematical explanation for SCIs. To address these problems, we propose a novel blind quality assessment method based on macro-micro modeling of tensor domain dictionary for SCIs in this paper. In the proposed method, the tensor decomposition is explored first to avoid the loss of color information, and then a target dictionary is learned more effectively with the principal components. Furthermore, a macro-micro model is established to characterize the micro and macro features in the target dictionary space, which is capable of providing a systematic mathematical interpretation for feature extraction. For the micro features, a log-normal pooling scheme is designed to enhance the effectiveness of feature aggregation by analyzing the particularity of the statistical distribution of sparse codes. Additionally, the statistical properties are mainly discussed and studied based on the Bernoulli law of large numbers, and then a reliable macro feature is generated to describe the relationship between the statistical distribution and quality degradation of SCIs. Experimental results determined by using three public SCI databases show that the proposed method can perform better than relevant existing methods in the prediction of the visual quality of SCIs, especially in terms of the generalization for distortion type and interpretability for feature generation.

Considering multidimensional structure of the multi-exposure images, a new Tensor product and Tensor-singular value decomposition based Multi-Exposure image Fusion (TT-MEF) method is proposed. The main innovation of this work is to explore a new feature representation of multi-exposure images in the new tensor domain and design the fusion strategy on this basis. Specifically, the luminance and the chrominance channels are fused separately to maintain color consistency. For the luminance fusion, the luminance channel of multi-exposure images is divided into two parts, that is, de-mean term and mean term. The de-mean term is represented as a tensor to extract the feature. Then, the tensor product and tensor-singular value decomposition (T-SVD) are used to design a tensor feature extractor. Furthermore, a fusion strategy of the de-mean term is presented according to the visual saliency model, and a fusion strategy of the mean term is defined by the local and the global visual weights to control counterpoise between the local and global luminance. For the chrominance fusion, a new fusion strategy is also designed by the tensor product and T-SVD, similar to the luminance fusion. Finally, the fused image is obtained by combining the luminance and chrominance fusion. Experimental results show that the proposed TT-MEF method generally outperforms the existing state-of-the-art in terms of subjective visual quality and objective evaluation.

Scene Text Image Super-resolution (STISR) aims to enhance the resolution of images containing text within a scene, making the text more readable and easier to recognize. This technique has broad applications in numerous fields such as autonomous driving, document scanning, image retrieval, and so on. However, most existing STISR methods have not fully exploited the multi-scale structural and semantic information within scene text images. As a result, the restored text image quality is not sufficient, significantly impacting subsequent tasks such as text detection and recognition. Hence, this paper proposes a novel scheme that leverages multi-scale structural and semantic priors to efficiently guide text semantic restoration, ultimately yielding high-quality text images. First, a multi-scale interaction attention (MSIA) module is designed to capture location-specific details of various-scale structural features and facilitate the recovery of semantic information. Second, a multi-scale prior learning module (MSPLM) is developed. Within this module, skip connections are employed among codecs to strengthen both structural and semantic prior features, thereby enhancing the up-sampling and reconstruction capabilities. Finally, building upon the MSPLM, cascaded encoders are connected through residual connections to further enrich the multi-scale features and bolster the representational capacity of the prior. Experiments conducted on the standard TextZoom dataset demonstrate that the average recognition accuracies of three evaluators—ASTER, CRNN, and MORAN—are 64.4%, 53.5%, and 60.8%, respectively, surpassing most existing methods, including the state-of-the-art ones.