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Recently, most dehazed image quality assessment (DQA) methods have focused on estimating remaining haze and omitting distortion impact from the side effect of dehazing algorithms, which leads to their limited performance. Addressing this problem, we propose a method for learning both visibility and distortion-aware features no-reference (NR) dehazed image quality assessment (VDA-DQA). Visibility-aware features are exploited to characterize clarity optimization after dehazing, including the brightness-, contrast-, and sharpness-aware features extracted by the complex contourlet transform (CCT). Then, distortion-aware features are employed to measure the distortion artifacts of images, including the normalized histogram of the local binary pattern (LBP) from the reconstructed dehazed image and the statistics of the CCT subbands corresponding to the chroma and saturation map. Finally, all the above features are mapped into quality scores by support vector regression (SVR). Extensive experimental results on six public DQA datasets verify the superiority of the proposed VDA-DQA method in terms of consistency with subjective visual perception and outperform state-of-the-art methods. The source code of VDA-DQA is available at https://github.com/li181119/VDA-DQA.

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Text recognition has been applied in many fields recently, such as robot vision, video retrieval, and scene understanding. However, minimal research has been conducted in the field of logistics wherein images of express sheets captured by cameras are mostly curved, distorted, and have low resolution. In this study, a new method is proposed to address the aforementioned research gap while simultaneously considering irregular and low-resolution English letters. The entire approach comprises a rectification module, a convolutional neural network (CNN) extractor, a semantic context module (SCM), a global context module (GCM), and a lightweight transformer decoder that can exhibit improved training speed. In particular, we propose the idea of context modeling in our proposed method. (1) The proposed SCM is introduced to capture full-image dependencies and generates rich semantic context information. (2) We propose the GCM, which not only enhances long-range dependencies from the output of SCM but also outputs abundant pixel information to the self-attention decoder. (3) To solve the low-resolution text recognition problem in a large number of express sheet scenes, we propose Chinese datasets for improving intelligent logistics. Experiments conducted on six public benchmarks demonstrate that the developed method achieves better robustness to low-resolution and irregular text images.

An aim of completely blind image quality assessment (BIQA) is to develop algorithms which can grade image quality without any prior knowledge of the images. Here, a new contourlet energy statistics based completely on blind opinion‐unaware BIQA (OU‐BIQA) method is proposed, which can predict the perceptual severity of a range of image distortion types without requiring any prior knowledge. According to the energy distribution of the contourlet sub‐bands of natural images in log‐domain, the lower‐scale sub‐band energy can be predicted by the corresponding higher‐scale sub‐band energies of distorted images. A quality model is then constructed by quantifying the difference between predicted energy and realistic energy. Meanwhile, an effective method for adjusting and compensating an undesired distortion is integrated into the quality model. Experimental results show that the proposed new method outperforms state‐of‐the‐art OU‐BIQA models on relevant portions of TID2013 database, and is competitive on the LIVE IQA database. Moreover, the proposed model is very fast, suggesting a real‐time solution to high‐performance BIQA.