Image Super-resolution Techniques Research Articles

Multi-dimensional attention-aided transposed ConvBiLSTM network for hyperspectral image super-resolution

Hyperspectral (HS) image always suffers from the deficiency of low spatial resolution, compared with conventional optical image types, which has limited its further applications in remote sensing areas. Therefore, HS image super-resolution (SR) techniques are broadly employed in order to observe finer spatial structures while preserving the spectra of ground covers. In this paper, a novel multi-dimensional attention-aided transposed convolutional long-short term memory (LSTM) network is proposed for single HS image super-resolution task. The proposed network employs the convolutional bi-directional LSTM for the purpose of local and non-local spatial–spectral feature explorations, and transposed convolution for the purpose of image amplification and reconstruction. Moreover, a multi-dimensional attention module is proposed, aiming to capture the salient features on spectral, channel, and spatial dimensions, simultaneously, to further improve the learning abilities of network. Experiments on four commonly-used HS images demonstrate the effectiveness of this approach, compared with several state-of-the-art deep learning-based SR methods.

A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images.

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.

A method of degradation mechanism-based unsupervised remote sensing image super-resolution

Remote sensing image (RSI) super-resolution (SR) is an efficient and low-cost technique to achieve high-resolution and high-quality reconstruction images. The quality of RSI SR reconstruction is affected by the prior information contained in the degradation model. Therefore, studying how to incorporate more RSI degradation prior into the degradation model is crucial. This article presents an approach to design the degradation model by extracting degradation factors from the perspective of remote sensing imaging mechanisms. It includes two aspects: simulating the atmospheric scattering effect through RGB channel weights downsampling and the comprehensive degradation effect of the remote sensing imaging platform through combined blurring. Furthermore, we proposed a high-performance RSI SR network based on degradation mechanism (RSN-DM), which includes a degrader D and a generator G, to employ remote sensing prior fully. We conducted experiments on the UC Merced Land-Use and WPU-RESIS45 datasets, demonstrating that our proposed method is effective. Our method achieves state-of-the-art (SOTA) performance in quantitative evaluation and visual quality. Finally, we apply the proposed degradation model to other networks to further validate the model's effectiveness. Therefore, the degradation model proposed in this paper can enhance the performance of remote sensing image super-resolution techniques in practical applications.

MATLAB-based Image Super-resolution Technique

This thesis provides an in-depth study of MATLAB-based image super-resolution techniques, covering the application of traditional methods (Fourier Transform, Wavelet Transform, Sparse Representation, Bicubic) and deep learning methods (SwinIR, NAFnet, Path-Restore). In the section on the background and significance of image super-resolution techniques, the wide range of applications of MATLAB in the field of image processing is explored. In the technical foundation section, the principles of image super-resolution technology are detailed, relevant image processing functions and tools in MATLAB are introduced, and the key steps of the technology are described. In the practical application, the comparative analysis of different methods is demonstrated through an example of image super-resolution in a real scene, including the effect diagram and MATLAB code. In the conclusion, the advantages and disadvantages of each method are summarized, which provides a certain reference for the future development of image super-resolution technology.

A High-Performance Accelerator for Real-Time Super-Resolution on Edge FPGAs

In the digital era, the prevalence of low-quality images contrasts with the widespread use of high-definition displays, primarily due to low-resolution cameras and compression technologies. Image super-resolution (SR) techniques, particularly those leveraging deep learning, aim to enhance these images for high-definition presentation. However, real-time execution of deep neural network (DNN)-based SR methods at the edge poses challenges due to their high computational and storage requirements. To address this, field-programmable gate arrays (FPGAs) have emerged as a promising platform, offering flexibility, programmability, and adaptability to evolving models. Previous FPGA-based SR solutions have focused on reducing computational and memory costs through aggressive simplification techniques, often sacrificing the quality of the reconstructed images. This paper introduces a novel SR network specifically designed for edge applications, which maintains reconstruction performance while managing computation costs effectively. Additionally, we propose an architectural design that enables the real-time and end-to-end inference of the proposed SR network on embedded FPGAs. Our key contributions include a tailored SR algorithm optimized for embedded FPGAs, a DSP-enhanced design that achieves a significant four-fold speedup, a novel scalable cache strategy for handling large feature maps, optimization of DSP cascade consumption, and a constraint optimization approach for resource allocation. Experimental results demonstrate that our FPGA-specific accelerator surpasses existing solutions, delivering superior throughput, energy efficiency, and image quality.

Optimizing OCR Performance for Programming Videos: The Role of Image Super-Resolution and Large Language Models

The rapid evolution of video programming tutorials as a key educational resource has highlighted the need for effective code extraction methods. These tutorials, varying widely in video quality, present a challenge for accurately transcribing the embedded source code, crucial for learning and software development. This study investigates the impact of video quality on the performance of optical character recognition (OCR) engines and the potential of large language models (LLMs) to enhance code extraction accuracy. Our comprehensive empirical analysis utilizes a rich dataset of programming screencasts, involving manual transcription of source code and the application of both traditional OCR engines, like Tesseract and Google Vision, and advanced LLMs, including GPT-4V and Gemini. We investigate the efficacy of image super-resolution (SR) techniques, namely, enhanced deep super-resolution (EDSR) and multi-scale deep super-resolution (MDSR), in improving the quality of low-resolution video frames. The findings reveal significant improvements in OCR accuracy with the use of SR, particularly at lower resolutions such as 360p. LLMs demonstrate superior performance across all video qualities, indicating their robustness and advanced capabilities in diverse scenarios. This research contributes to the field of software engineering by offering a benchmark for code extraction from video tutorials and demonstrating the substantial impact of SR techniques and LLMs in enhancing the readability and reusability of code from these educational resources.

Deep local-to-global feature learning for medical image super-resolution

Medical images play a vital role in medical analysis by providing crucial information about patients’ pathological conditions. However, the quality of these images can be compromised by many factors, such as limited resolution of the instruments, artifacts caused by movements, and the complexity of the scanned areas. As a result, low-resolution (LR) images cannot provide sufficient information for diagnosis. To address this issue, researchers have attempted to apply image super-resolution (SR) techniques to restore the high-resolution (HR) images from their LR counterparts. However, these techniques are designed for generic images, and thus suffer from many challenges unique to medical images. An obvious one is the diversity of the scanned objects; for example, the organs, tissues, and vessels typically appear in different sizes and shapes, and are thus hard to restore with standard convolution neural networks (CNNs). In this paper, we develop a dynamic-local learning framework to capture the details of these diverse areas, consisting of deformable convolutions with adjustable kernel shapes. Moreover, the global information between the tissues and organs is vital for medical diagnosis. To preserve global information, we propose pixel–pixel and patch–patch global learning using a non-local mechanism and a vision transformer (ViT), respectively. The result is a novel CNN-ViT neural network with Local-to-Global feature learning for medical image SR, referred to as LGSR, which can accurately restore both local details and global information. We evaluate our method on six public datasets and one large-scale private dataset, which include five different types of medical images (i.e., Ultrasound, OCT, Endoscope, CT, and MRI images). Experiments show that the proposed method achieves superior PSNR/SSIM and visual performance than the state of the arts with competitive computational costs, measured in network parameters, runtime, and FLOPs. What is more, the experiment conducted on OCT image segmentation for the downstream task demonstrates a significantly positive performance effect of LGSR.

Super resolution-based methodology for self-supervised segmentation of microscopy images.

Data-driven Artificial Intelligence (AI)/Machine learning (ML) image analysis approaches have gained a lot of momentum in analyzing microscopy images in bioengineering, biotechnology, and medicine. The success of these approaches crucially relies on the availability of high-quality microscopy images, which is often a challenge due to the diverse experimental conditions and modes under which these images are obtained. In this study, we propose the use of recent ML-based image super-resolution (SR) techniques for improving the image quality of microscopy images, incorporating them into multiple ML-based image analysis tasks, and describing a comprehensive study, investigating the impact of SR techniques on the segmentation of microscopy images. The impacts of four Generative Adversarial Network (GAN)- and transformer-based SR techniques on microscopy image quality are measured using three well-established quality metrics. These SR techniques are incorporated into multiple deep network pipelines using supervised, contrastive, and non-contrastive self-supervised methods to semantically segment microscopy images from multiple datasets. Our results show that the image quality of microscopy images has a direct influence on the ML model performance and that both supervised and self-supervised network pipelines using SR images perform better by 2%-6% in comparison to baselines, not using SR. Based on our experiments, we also establish that the image quality improvement threshold range [20-64] for the complemented Perception-based Image Quality Evaluator(PIQE) metric can be used as a pre-condition by domain experts to incorporate SR techniques to significantly improve segmentation performance. A plug-and-play software platform developed to integrate SR techniques with various deep networks using supervised and self-supervised learning methods is also presented.

Comparative Assessment of Image Super-Resolution Techniques for Spatial Downscaling of Gridded Rainfall Data

Comparative Assessment of Image Super-Resolution Techniques for Spatial Downscaling of Gridded Rainfall Data

A new Legendre wavelet filter-based image super-resolution technique

A new Legendre wavelet filter-based image super-resolution technique

Tensor Ring Based Image Enhancement.

Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement. In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm. This enables the method to do the super-resolution and de-noising simultaneously. Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations.

Gradient residual attention network for infrared image super-resolution

Image super-resolution techniques overcome the limitations of physical limits on infrared imaging systems and allow higher-resolution images of targets to be obtained on top of existing systems. In this paper, we proved that infrared images are more reconstructed than visible images based on data compression. We propose a CNN-based Gradient Residual Attention Network (GRAN) for infrared image super-resolution. Specifically, the residual dense module (RDB) is used to acquire depth features and the gradient operator (GO) gains fine-grained detail features. Meanwhile, the 3D attention block (3DAB) learns features' channel and spatial correlation to selectively capture more useful informative features. The experimental results show that the proposed method performs well compared to popular image super-resolution methods in terms of quantitative metrics and visual quality.

Lightweight image super-resolution for IoT devices using deep residual feature distillation network

The 5th industrial revolution is characterized by an extensive interconnection of embedded devices, which offer a range of services, including the monitoring of their environments. Images captured from remote cameras require enhancements for effective analysis. Despite recent progress in single-image super-resolution techniques by yielding impressive results through deep convolutional neural networks, the complexity of these advanced models renders them impractical for use on miniaturized Internet of Things (IoT) devices, primarily due to their limited computational capabilities and memory constraints. Furthermore, the rapid evolution of IoT devices necessitates efficient image super-resolution techniques, while existing advanced methods, based on deep convolutional neural networks, are too resource-intensive for these devices, and this gap highlights the need for a more suitable solution. In this study, we introduce a lightweight, efficient super-resolution model specifically designed for IoT devices. This model incorporates a novel deep residual feature distillation block (DRFDB), which leverages a depthwise-separable convolution block (DCB) for effective feature extraction. The focus is on reducing computational and memory demands without compromising on image quality. The proposed DCB extracts coarse features from given input features as calculation units, using two operations, depthwise and pointwise convolutions. These two operations are able to significantly reduce the number of parameters and floating-point operations while maintaining a PSNR value higher than the 90% threshold. We modify the proposed DCB and introduce a multi-kernel depthwise-separable convolution block (MKDCB) to fine-tune the model. The experiments, conduct on various standard datasets such as DIV2K, Set5, Set14, Urban100, and Manga109 by demonstrating that our model significantly outperforms existing methods in terms of both image quality and computational efficiency. The model shows improved performance metrics like PSNR, while requiring fewer parameters and less memory usage, making it highly suitable for IoT applications. This study presents a breakthrough in super-resolution for IoT devices, balancing high-quality image reconstruction with the limited resources of these devices.

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images.

Research on Image Up-scaling and Super-resolution Based on Convolutional Neural Network

Image upsampling and super-resolution techniques are of great significance in many fields, which can improve image quality and enhance information extraction capabilities. Image upsampling and super-resolution techniques have wide applications in the real world, such as image quality enhancement, satellite and remote sensing images, and security and surveillance. This paper describes and analyzes a machine learning approach to up-scaling a low resolution images using Convolutional Neural Networks. We build on previous works on single image super-resolution. We train 3 structurally similar but different models and obtain an improvement of up to 3% compared to the more common image upscaling method, Bicubic Interpolation.

Single-image super-resolution based on an improved asymmetric Laplacian pyramid structure

Large-scale factor image super-resolution whose scale factor is greater than 4 is significant in real-world applications for single image super-resolution. The current image super-resolution techniques for large-scale factor, however, frequently upsample low-resolution images in a single pass, leading to edge artifacts in the reconstructed images. In this article, we provide an improved asymmetric Laplacian pyramid network to further realize large-scale factor image super-resolution and fully utilize various size features. A distinct architecture is applied at each layer of the pyramid for improving feature extraction. We extend the first layer of the pyramid with a lightweight transformer design, which enables the model to efficiently collect the contextual information in the sequence by utilizing the multi-headed attention mechanism. Additionally, we combine improved dense skip connections with recursive operations to form a deep dense recursive convolutional neural network that fuses low-level and high-level features while broadening the network's receptive field. The quantitative and qualitative analysis on benchmark dataset's demonstrate that our method provides superior performance in both PSNR and SSIM and is more in line with human visual perception.

Landsat-8 to Sentinel-2 Satellite Imagery Super-Resolution-Based Multiscale Dilated Transformer Generative Adversarial Networks

Image super-resolution (SR) techniques can improve the spatial resolution of remote sensing images to provide more feature details and information, which is important for a wide range of remote sensing applications, including land use/cover classification (LUCC). Convolutional neural networks (CNNs) have achieved impressive results in the field of image SR, but the inherent localization of convolution limits the performance of CNN-based SR models. Therefore, we propose a new method, namely, the dilated Transformer generative adversarial network (DTGAN) for the SR of multispectral remote sensing images. DTGAN combines the local focus of CNNs with the global perspective of Transformers to better capture both local and global features in remote sensing images. We introduce dilated convolutions into the self-attention computation of Transformers to control the network’s focus on different scales of image features. This enhancement improves the network’s ability to reconstruct details at various scales in the images. SR imagery provides richer surface information and reduces ambiguity for the LUCC task, thereby enhancing the accuracy of LUCC. Our work comprises two main stages: remote sensing image SR and LUCC. In the SR stage, we conducted comprehensive experiments on Landsat-8 (L8) and Sentinel-2 (S2) remote sensing datasets. The results indicate that DTGAN generates super-resolution (SR) images with minimal computation. Additionally, it outperforms other methods in terms of the spectral angle mapper (SAM) and learned perceptual image patch similarity (LPIPS) metrics, as well as visual quality. In the LUCC stage, DTGAN was used to generate SR images of areas outside the training samples, and then the SR imagery was used in the LUCC task. The results indicated a significant improvement in the accuracy of LUCC based on SR imagery compared to low-resolution (LR) LUCC maps. Specifically, there were enhancements of 0.130 in precision, 0.178 in recall, and 0.157 in the F1-score.

A Feasibility Study on Extension of Measurement Distance in Vision Sensor Using Super-Resolution for Dynamic Response Measurement.

The current civil infrastructure conditions can be assessed through the measurement of displacement using conventional contact-type sensors. To address the disadvantages of traditional sensors, vision-based sensor measurement systems have been derived in numerous studies and proven as an alternative to traditional sensors. Despite the benefits of the vision sensor, it is well known that the accuracy of the vision-based displacement measurement is largely dependent on the camera extrinsic or intrinsic parameters. In this study, the feasibility study of a deep learning-based single image super-resolution (SISR) technique in a vision-based sensor system is conducted to alleviate the low spatial resolution of image frames at long measurement distance ranges. Additionally, its robustness is evaluated using shaking table tests. As a result, it is confirmed that the SISR can reconstruct definite images of natural targets resulting in an extension of the measurement distance range. Additionally, it is determined that the SISR mitigates displacement measurement error in the vision sensor-based measurement system. Based on this fundamental study of SISR in the feature point-based measurement system, further analysis such as modal analysis, damage detection, and so forth should be continued in order to explore the functionality of SR images by applying low-resolution displacement measurement footage.

Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution.

Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are designed to restore high-frequency image details and enhance imaging resolution through the use of rapid and lightweight network architectures. Existing SISR methodologies face the challenge of striking a balance between performance and computational costs, which hinders the practical application of SISR methods. In response to this challenge, the present study introduces a lightweight network known as the Spatial and Channel Aggregation Network (SCAN), designed to excel in image super-resolution (SR) tasks. SCAN is the first SISR method to employ large-kernel convolutions combined with feature reduction operations. This design enables the network to focus more on challenging intermediate-level information extraction, leading to improved performance and efficiency of the network. Additionally, an innovative 9 × 9 large kernel convolution was introduced to further expand the receptive field. The proposed SCAN method outperforms state-of-the-art lightweight SISR methods on benchmark datasets with a 0.13 dB improvement in peak signal-to-noise ratio (PSNR) and a 0.0013 increase in structural similarity (SSIM). Moreover, on remote sensing datasets, SCAN achieves a 0.4 dB improvement in PSNR and a 0.0033 increase in SSIM.

Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super-resolution techniques based on deep learning. Nevertheless, most of the methods only consider SR with a few integer scale factors, which limits the application of the SR techniques to real-world problems. Recently, the methods to achieve arbitrary-scale super-resolution via a single model have attracted much attention. However, there is no work to thoroughly analyze the arbitrary-scale methods based on deep learning. In this work, we present a comprehensive and systematic review of 45 existing deep learning-based methods for arbitrary-scale image and video SR. We first classify the existing SR methods according to the resolved scale factors. Furthermore, we propose an in-depth taxonomy for state-of-the-art methods based on the core problem of how to achieve arbitrary-scale super-resolution, i.e., how to perform arbitrary-scale upsampling. Moreover, the performance of existing arbitrary-scale SR methods is compared, and their advantages and limitations are analyzed. We also provide some guidance for the selection of these methods in different real-world applications. Finally, we briefly discuss the future directions of arbitrary-scale super-resolution, which shows some inspirations for the progress of subsequent works on arbitrary-scale image and video super-resolution tasks. The repository of this work is available at https://github.com/Weepingchestnut/Arbitrary-Scale-SR.