In the task of integrated circuit micrograph acquisition, image super-resolution reconstruction technology can significantly enhance acquisition efficiency. With the advancement of deep learning techniques, the performance of image super-resolution r...
Small ships in remote sensing images have blurred details and are difficult to detect. Existing algorithms usually detect small ships based on predefined anchors with different sizes. However, limited by the number of different sizes, it is difficult...
Transformers have performed better than traditional convolutional neural networks (CNNs) for image super-resolution (SR) reconstruction in recent years. Currently, shifted window multi-head self-attention based on the swin transformer is a typical me...
Video super-resolution can generate corresponding to high-resolution video frames from a plurality of low-resolution video frames which have rich details and temporally consistency. Most current methods use two-level structure to reconstruct video fr...
Video super-resolution aims to generate high-resolution video sequences with realistic details from existing low-resolution video sequences. However, most existing video super-resolution models require substantial computational power and are not suit...
Recently, deep-learning-based image super-resolution methods have made remarkable progress. However, most of these methods do not fully exploit the structural feature of the input image, as well as the intermediate features from the intermediate laye...
Single-image super-resolution (SISR) seeks to elucidate the mapping relationships between low-resolution and high-resolution images. However, high-performance network models often entail a significant number of parameters and computations, presenting...
Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model’s overall per...
Image super-resolution (SR) algorithms based on deep learning yield good visual performances on visible images. Due to the blurred edges and low contrast of infrared (IR) images, methods transferred directly from visible images to IR images have a po...
In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems...
In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the...
As small commodity features are often few in number and easily occluded by hands, the overall detection accuracy is low, and small commodity detection is still a great challenge. Therefore, in this study, a new algorithm for occlusion detection is pr...
Due to the inadequacy in utilizing complementary information from different modalities and the biased estimation of degraded parameters, the unsupervised hyperspectral super-resolution algorithm suffers from low precision and limited applicability. T...
In recent years, great progress has been made in the field of super-resolution (SR) reconstruction based on deep learning techniques. Although image SR techniques show strong potential in image reconstruction, the effective application of these techn...
Remote sensing image (RSI) super-resolution plays a critical role in improving image details and reducing costs associated with physical imaging devices. However, existing super-resolution methods are not applicable to resource-constrained edge devic...
Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insuf...
The acquisition of high-resolution (HR) digital bathymetric models (DBMs) is crucial for oceanic research activities. However, obtaining HR DBM data is challenging, which has led to the use of super-resolution (SR) methods to improve the DBM’s...
Deep convolutional neural networks have demonstrated significant performance improvements in face super-resolution tasks. However, many deep learning-based approaches tend to overlook the inherent structural information and feature correlation across...
Super-resolution (SR) images based on deep networks have achieved great accomplishments in recent years, but the large number of parameters that come with them are not conducive to use in equipment with limited capabilities in real life. Therefore, w...
Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full u...
Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for...
The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dis...
Image super-resolution (SR) techniques aim to enhance the clarity and realism of images. Recently, a wide range of excellent SR algorithms with powerful characterization capabilities have emerged and are widely used. However, there are still challeng...
The scale of digital elevation models (DEMs) is vital for terrain analysis, surface simulation, and other geographic applications. Compared to traditional super-resolution (SR) methods, deep convolutional neural networks (CNNs) have shown great succe...
One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the co...
Recently, transformer-based face super-resolution (FSR) approaches have achieved promising success in restoring degraded facial details due to their high capability for capturing both local and global dependencies. However, while existing methods foc...
In this paper, we propose an end-to-end single-image super-resolution neural network by leveraging hybrid multi-scale features of images. Different from most existing convolutional neural network (CNN) based solutions, our proposed network depends on...
Light field (LF) cameras can capture the intensity and angle information of any scene in one single shot, and are widely used in virtual reality, refocusing, de-occlusion, depth estimation, etc. However, the fundamental limitation between angular and...
Recent years have witnessed the great success of image super-resolution based on deep learning. However, it is hard to adapt a well-trained deep model for a specific image for further improvement. Since the internal repetition of patterns is widely o...
Acquiring high-resolution digital elevation models (DEMs) over across extensive regions remains challenging due to high costs and insufficient detail, creating demand for super-resolution (SR) techniques. However, existing DEM SR methods still rely o...
Light Field (LF) cameras can capture angular and spatial information simultaneously, making them suitable for a wide range of applications such as refocusing, disparity estimation, and virtual reality. However, the limited spatial resolution of the L...
Deep learning is an important research topic in the field of image super-resolution. Problematically, the performance of existing hyperspectral image super-resolution networks is limited by feature learning for hyperspectral images. Nevertheless, the...
Hyperspectral imaging (HSI) captures the same scene across multiple spectral bands, providing richer spectral characteristics of materials than conventional RGB images. The spectral reconstruction task seeks to map RGB images into hyperspectral image...
Multi-frame super-resolution (MFSR) generates a super-resolution (SR) image from a burst consisting of multiple low-resolution images. Burst Super-Resolution Transformer (BSRT) is a state-of-the-art deep learning model for MFSR. However, in this stud...
Video super-resolution reconstruction is the process of reconstructing low-resolution video frames into high-resolution video frames. Most of the current methods use motion estimation and motion compensation to extract temporal series information, bu...
Recently, many super-resolution reconstruction (SR) feedforward networks based on deep learning have been proposed. These networks enable the reconstructed images to achieve convincing results. However, due to a large amount of computation and parame...
Remote sensing images often have limited resolution, which can hinder their effectiveness in various applications. Super-resolution techniques can enhance the resolution of remote sensing images, and arbitrary resolution super-resolution techniques p...
The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data...
Single-image super-resolution (SISR) methods based on convolutional neural networks (CNNs) have achieved breakthrough progress in reconstruction quality. However, their high computational costs and model complexity have limited their applications in...
The mechanical LiDAR sensor is crucial in autonomous vehicles. After projecting a 3D point cloud onto a 2D plane and employing a deep learning model for computation, accurate environmental perception information can be supplied to autonomous vehicles...
To address the issues of insufficient feature utilization in high-entropy regions (such as complex textures and edges), difficulty in detail recovery, and excessive model parameters with high computational complexity in existing remote sensing image...
To address the issue of insufficient resolution in remote sensing images due to limitations in sensors and transmission, this paper proposes a multi-scale feature fusion model, MSFANet, based on the Swin Transformer architecture for remote sensing im...
In scenes with large inter-frame motion variations, distant targets, and blurred targets, the lack of inter-frame alignment can greatly affect the effectiveness of subsequent video super-resolution reconstruction. How to perform inter-frame alignment...
This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper...
Recently, optical remote-sensing images have been widely applied in fields such as environmental monitoring and land cover classification. However, due to limitations in imaging equipment and other factors, low-resolution images that are unfavorable...
Effective aggregation of temporal information of consecutive frames is the core of achieving video super-resolution. Many scholars have utilized structures such as sliding windows and recurrences to gather the spatio-temporal information of frames. H...
High-quality remote sensing images play important roles in the development of ecological indicators’ mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land...
Satellite mapping of buildings and built-up areas used to be delineated from high spatial resolution (e.g., meters or sub-meters) and middle spatial resolution (e.g., tens of meters or hundreds of meters) satellite images, respectively. To the best o...
Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical ap...
Image super-resolution (ISR) technology aims to enhance resolution and improve image quality. It is widely applied to various real-world applications related to image processing, especially in medical images, while relatively little appliedto anime i...
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