In this paper, we propose a blind image quality assessment (BIQA) method using self-attention and a recurrent neural network (RNN); this approach can effectively capture both local and global information from an input image. The implementation of our constructed deep no-reference (NR) assessment framework does not rely on any convolutional operations. First, the capture step for obtaining locally significant information is performed by a self-attention operation inside a divided window. Second, we design a serialized feature input memory subnetwork to fuse the global features of the image. Finally, all the integrated features are uniformly mapped to the target score. The experimental results obtained on publicly available benchmark IQA databases show that our approach outperforms other state-of-the-art algorithms.

Enhancing low-light image visibility is a critical task in computer vision since it helps to improve input for high-level algorithms. High-quality images typically have clear structural information. In previous studies, due to the lack of proper structural guidance, restored images had some problems, such as unclear structural areas and overexposed or underexposed local areas. To address the above problems, in this paper, we introduce a coefficient of variation (COV) with excellent performance in maintaining structural information, and then we propose a low-light image enhancement method that utilizes the COV to extract structural information from images. First, we apply a traditional retinex model to estimate both reflectance and illumination. Second, we use the COV to indicate the degree of dispersion of the input sample, which enables us to obtain a robust structure-distinguishing weight map for low-light images. The weight map is adaptively divided to obtain a structural weight map, which is then used to enhance the gradient image. This process is applied before the reflectance layer of the retinex model. Finally, the result is obtained by using the block coordinate descent method. According to extensive experiments, outstanding results can be achieved by our proposed method in terms of both subjective and objective evaluation metrics in comparison with other state-of-the-art methods. The source code is available at our website ^{1 1}https://github.com/bbxavi/spcv22 .

Generally, 360-degree images have absolute senses of reality and three-dimensionality, providing a wide range of immersive interactions. Due to the novel rendering and display technology of 360-degree images, they have more complex perceptual characteristics than other images. It is challenging to perform comprehensive image quality assessment (IQA) learning by simply stacking multichannel neural network architectures for pre/postprocessing, compression, and rendering tasks. To thoroughly learn the global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a joint architecture with user perception and an efficient transformer dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cube map projection (CMP) image. Furthermore, the proposed 360-degree NRIQA method includes a saliency map-based non-overlapping self-attention selection module and a U-shaped transformer (U-former)-based feature extraction module to account for perceptual region importance and projection distortion. The transformer-based architecture and the weighted average technique are jointly utilized for predicting local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-the-art methods in NR 360-degree image quality evaluation cases. Furthermore, a cross-database evaluation and an ablation study also demonstrate the inherent robustness and generalization ability of the proposed model.

Object detection plays a key role in helping to enable industrial quality control and safety monitoring. This article introduces a lightweight and efficient transformer-based object detection network called the encoder-free DEtection TRansformer (EF-DETR). This novel architecture enhances the DETR model through a redesigned network structure, leading to improved accuracy in object detection and a more lightweight network. To address the issue of suboptimal object detection accuracy, especially for small objects in the DETR model, we introduce a multiscale feature extractor and a high-efficiency feature fusion module. These components facilitate the direct extraction of fine-grained features, thereby enabling effective object detection. Departing from the use of a high-complexity encoder structure, we explore the utilization of an encoder-free neck structure to reduce the network's computational complexity. In addition, to expedite convergence, denoising training is incorporated into the decoder. This article presents extensive experiments, and the EF-DETR demonstrates strong performance on the MS COCO2017 dataset compared to other popular models.

Detecting small objects in remote sensing images poses significant challenges to the field of computer vision, primarily stemming from the complexity of backgrounds, limitations in pixel resolution, and information loss during the feature fusion process. While general object detection has significantly advanced in recent years, remote sensing small object detection remains an unsolved problem, with existing frameworks struggling to achieve high performance at small scales. In this article, we propose a novel framework called the boundary-aware feature fusion network (BAFNet), which significantly enhances the model’s ability to represent and locate small objects precisely within complex remote sensing scenarios. First, a dual-stream attention fusion module captures complementary foreground and background cues through bidirectional context modeling. Jointly attending to objects and their surroundings enhances discriminative power for distinguishing small objects. Additionally, we incorporate a boundary-aware branch to better preserve crucial detailed information vital for small-scale objects. This auxiliary component supervises the fusion of contextual semantics and spatial information, aiding in retaining critical boundary details that are prone to loss during cross-layer feature fusion. We conducted experiments on the challenging AI-TOD, VisDrone, DIOR, and LEVIR-Ship datasets. The results demonstrate the superiority of our approach over other state-of-the-art (SOTA) object detection methods, particularly in terms of precisely identifying small objects within remote sensing images. The code is available at https://github.com/ooo1128/BAFNet .