Single Image Super-Resolution Network Based on Multiple- Scale Self-Similarity Module

Recent research on single image super-resolution (SISR) using convolutional neural networks (CNNs) with the utilization of residual structures and attention mechanisms to utilize image features has demonstrated excellent performance. However, previous SISR techniques mainly integrated extracted image features within a deep or wide network architecture, ignoring the interaction between multiscale features and the diversity of features. At the same time, SISR is also a typical ill-posed problem in that it allows for several predictions for a given LR image. These problems limit the great learning ability of CNNs. To solve these problems, we propose a closed-loop residual attention network (CLRAN) to extract and interact with all the available diversity of features features efficiently and limit the space of possible function solutions. Specifically, we design an enhanced residual attention block (ERA) to extract features, and it dynamically assigns weight to the internal attention branches. The ERA combines multi-scale block (MSB) and enhanced attention mechanism (EAM) base on the residual module. The MSB adaptively detects multiscale image features of different scales by using different 3 × 3 convolution kernels. The EAM combines multi-spectral channel attention (MSCA) and spatial attention (SA). Therefore, the EAM extracts different frequency component information and spatial information to utilize the diversity features. Furthermore, we apply the progressive network architecture and learn an additional map for model monitoring, which forms a closed-loop with the mapping already learned by the LR to HR function. Extensive experiments demonstrate that our CLRAN outperforms the state-of-the-art SISR methods on public datasets for both ×4 and ×8, proving its accuracy and visual perception.

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 success in DEM SR. However, in terms of these CNN-based SR methods, the features extracted by the stackable residual modules cannot be fully utilized as the depth of the network increases. Therefore, our study proposes an enhanced residual feature fusion network (ERFFN) for DEM SR. The designed residual fusion module groups four residual modules to make better use of the local residual features. Meanwhile, the residual structure is refined by inserting a lightweight enhanced spatial residual attention module into each basic residual block to further strengthen the efficiency of the network. Considering the continuity of terrain features, terrain weight modules are integrated into the loss module. Based on two large-scale datasets, our ERFFN shows a 10–20% reduction in the mean absolute error and the lowest error in terrain features, such as slope, demonstrating the superiority of an ERFFN-based DEM SR over state-of-the-art methods. Finally, to demonstrate potential value in real-world applications, we deploy the ERFFN to reconstruct a large geographic area covering 44,000 km2 which contains missing parts.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods. The 1-bit quantization is an extreme lightweight method which can reduce the calculation amount of the model in an extreme manner and is friendly to hardware such as edge devices. Most existing binary quantization approaches lead to a large information loss during forward propagation, especially in detailed color information (e.g., edge, texture, and contrast). The loss of color information makes modern binary methods unsuitable for SISR tasks. We think the loss occurs because these methods typically utilize a uniform threshold to quantize the weights and activations. Thus, in this article, we thoroughly analyze the difference between normal classification tasks and SISR tasks, and present a binarization scheme based on local means. The proposed method can maintain more detailed information in feature maps using dynamic thresholds during quantization. Specifically, each value in the full precision activations has a corresponding threshold during the quantization process, and those thresholds are determined by the full precision values of the surroundings. In addition, a gradient approximator is introduced to adaptively optimize the gradient for updating binary weights. We then verify the effectiveness of our method for training binary networks on several SISR benchmarks including VDSR and SRResNet. Experimental results show that the proposed method can outperform the state-of-the-art algorithms to obtain binary networks for image super-resolution with better peak signal-to-noise ratio (PSNR) values and visual quality.

Recently deep convolutional neural network (CNN) has made significant achievement in Single Image Super-Resolution (SISR). Most CNN-based SISR methods used the default L2 norm of the error. However, for Hyperspectral Image (HSI), this loss function may bring spectral inconsistencies. The main reason is that most methods did not pay much attention to spectral loss. To HSI, the loss function should capture not only spatial information but also spectral consistency. In this paper, a Multi-Losses Function Network (MLFN) simultaneously considering spatial and spectral information is proposed, and is composed of two parts: one is Concatenate Dense Residual Network (CDRN), and the other is Loss Network (LN). CDRN is an image reconstruction network which can utilize the hierarchical features extracted from the low-resolution image. LN includes pixel-wise spatial loss and spectral loss which drive the learning of the entire reconstruction model. The experimental results prove that the proposed MLFN can enhance spatial resolution with the consistency of the spectrum of HSI preserved.

Single image super-resolution (SISR) with deep convolutional neural networks has recently attracted increasing attention due to its potentials to generate rich details. To obtain better fidelity and visual quality, most of existing methods are of heavy design with the depth of network. However, the lack of high-frequency information in the deep network and the limit of non-local context relations of pixels remain the core issue. In addition, there are many arbitrary-scale image super-resolution tasks, so it is a focus that provides an effective and efficient model for super resolution with arbitrary-scale. To this end, we propose a Feature Preserving and Enhancing Network (FPEN) based on implicit representation, which is aims to preserving high-frequency information in deep network and enhancing non-local contextual features of pixels to output more realistic images at arbitrary scales. In particular, our proposed High Frequency Preserving Block (HFPB) can divide the features into high-frequency components and low-frequency components, and allocate more operations to high-frequency components to ensure that high-frequency components can be preserved in the network. Since high-frequency information contains the details and texture of the image, it can restore the finer details of the image. Moreover, Pixel Continuity Attention (PiCA) module our proposed utilizes visual cues observed from pixels to adaptively recalibrate the pixel range that needs attention in the image to better achieve feature enhancement and generate smoother images. Extensive experiments conducted on benchmark SISR models and datasets show that Feature Preserving and Enhancing Network can be employed for various SISR tasks with arbitrary-scale to obtain the better visual quality than other state-of-the-art SR algorithms.

Lightweight single image super-resolution with attentive residual refinement network

Recently, the deep convolutional neural network (CNN) has made remarkable progress in single image super resolution(SISR). However, blindly using the residual structure and dense structure to extract features from LR images, can cause the network to be bloated and difficult to train. To address these problems, we propose a simple and efficient distilling with residual network(DRN) for SISR. In detail, we propose residual distilling block(RDB) containing two branches, while one branch performs a residual operation and the other branch distills effective information. To further improve efficiency, we design residual distilling group(RDG) by stacking some RDBs and one long skip connection, which can effectively extract local features and fuse them with global features. These efficient features beneficially contribute to image reconstruction. Experiments on benchmark datasets demonstrate that our DRN is superior to the state-of-the-art methods, specifically has a better trade-off between performance and model size.

The deep convolutional neural network has achieved great success in the Single Image Super-resolution task. It is obviously that among the well-known super-resolution methods, the deep learning-based algorithms show the most advanced performance. However, the most advanced algorithms currently use complex networks with a large number of parameters, which makes it difficult to apply deep learning algorithms on mobile devices. To solve this problem, we propose a lightweight dual-residual network(LDRN) for single image super-resolution, which has better reconstruction quality than most current advanced lightweight algorithms. Due to its fewer parameters and computational expense, real-time and mobile applications of our networks can be easily realized. On the basis of the residual module, we propose a new residual unit, which uses two depthwise separable(DW) convolution to obtain better balance between feature extraction capacity and lightweight performance. We further design a dual-stream residual block, which contains a multiplication branch and an addition branch. The dual-stream residual block can improve the reconstruction performance more effectively than expanding the network width. In addition, we also designed a new up-sampling module to simplify the previous up-sampling methods. Extensive experimental results show that our network has better reconstruction performance and lightweight performance than most existing state-of-the-art algorithms. Our code is available at https://github.com/Jiangyichun-cust/pytorch-LDRN.

Kernel-attended residual network for single image super-resolution

In recent years, attention mechanisms have shown great potential in various computer vision tasks. However, most existing methods focus on developing more complex attention modules for better performance, which inevitably increases the complexity of the model. To overcome performance and complexity tradeoffs, this paper proposes efficient spatial and channel attention (ESCA), a symmetric, comprehensive, and efficient attention module. By analyzing squeeze-and-excitation (SE), convolutional block attention module (CBAM), coordinate attention (CA), and efficient channel attention (ECA) modules, we abandon the dimension-reduction operation of SE module, verify the negative impact of global max pooling (GMP) on the model, and apply a local cross-channel interaction strategy without dimension reduction to learn attention. We not only care about the channel features of the image, we also care about the spatial location of the target on the image, and we take into account the effectiveness of channel attention, so we designed the symmetric ESCA module. The ESCA module is effective, as demonstrated by its application in the ResNet-50 classification benchmark. With 26.26 M parameters and 8.545 G FLOPs, it introduces a mere 0.14% increment in FLOPs while achieving over 6.33% improvement in Top-1 accuracy and exceeding 3.25% gain in Top-5 accuracy. We perform image classification and object detection tasks on ResNet, MobileNet, YOLO, and other architectures on popular datasets such as Mini ImageNet, CIFAR-10, and VOC 2007. Experiments show that ESCA can achieve great improvement in model accuracy at a very small cost, and it performs well among similar models.

Recently, deep convolutional neural networks (CNNs) are widely used in single image super-resolution (SISR) and have recorded impressive performance. However, most of the existing CNNs architectures can not fully utilize the correlation of feature maps in the middle layers, and abundant features of different levels are lost. Furthermore, convolution operation is limited by processing one local neighborhood at a time, which lacks global information. To address these issues, we propose the nested dense attention network (NDAN) for generating more refined and structured high-resolution images. Specifically, we propose nested dense structure (NDS) to better integrate features of different levels extracted from different layers. Besides that, in order to capture inter-channel dependencies more efficiently, we propose the adaptive channel attention module (ACAM) to adaptively rescale channel-wise features by automatically adjusting the weights of different receptive fields. Furthermore, to better explore the global-level context information, we design hybrid non-local module (HNLM) and hybrid non-local up-sampler (HNLU) to upscale the images by capturing spatial-wise long-distance dependencies and channel-wise long-distance correlation. Numerous experiments demonstrate the effectiveness of our model by achieving higher PSNR and SSIM scores and generating images with better structures against the state-of-the-art methods.

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super- resolution and surpass previous signal-processing based approaches. However, their performance is limited when applied to real photographs. The reason lies in their train- ing data: low-resolution (LR) images are obtained by bicu- bic interpolation of the corresponding high-resolution (HR) images. The applied convolution kernel significantly differs from real-world camera-blur. Consequently, while current CNNs well super-resolve bicubic-downsampled LR images, they often fail on camera-captured LR images. To improve generalization and robustness of deep super- resolution CNNs on real photographs, we present a ker- nel modeling super-resolution network (KMSR) that incor- porates blur-kernel modeling in the training. Our pro- posed KMSR consists of two stages: we first build a pool of realistic blur-kernels with a generative adversarial net- work (GAN) and then we train a super-resolution network with HR and corresponding LR images constructed with the generated kernels. Our extensive experimental vali- dations demonstrate the effectiveness of our single-image super-resolution approach on photographs with unknown blur-kernels.

Recently, deep convolutional neural networks (CNNs) have been demonstrated excellent performance on single image super-resolution (SISR). However, most deep learning based methods lack the ability to distinguish features in network. For image super-resolution, it is important to design an effective prior to learn the correlation of various feature. To solve this problem, we propose a non-local hierarchical residual network (NHRN) for SISR. Specifically, we introduce a non-local module to measure the self-similarity between each pixels in the feature map, and obtain a weight matrix guiding the deep network to find more precise relationship between L-R and HR images. Thus our method reconstruct images with a sharper edge. In addition, we employ group convolutions to build a hierarchical residual structure, which enable the network extract image features hierarchically. It can reduce the executive time while ensuring reconstruct image quality. Extensive experiments show that our NHRN achieves better accuracy and speed against state-of-the-art methods.

Recent years have witnessed great success of applying deep convolutional neural networks (CNNs) to single image super-resolution (SISR). However, most of these algorithms focus on increasing modeling capability through developing deeper and wider networks, improving the performance but at a cost of huge computation. Targeting at a better trade-off between efficiency and effectiveness, we propose ESASN, an efficient second-order attention spindle network for lightweight SISR. ESASN is built upon efficient second-order attention spindle (ESAS) blocks, each of which contains two well-designed new modules, efficient multi-scale (EMS) module and second-order attention (SOA) module. EMS reduces a considerable number of parameters while retaining the multi-scale structure to explore rich features. SOA further rescales the multi-scale feature maps, capturing the inter-dependencies among channels pixel-wisely with little additional cost. Both qualitative and quantitative experimental results demonstrate that the combination of EMS and SOA works out favorably for SISR, lifting the performance with fewer parameters. Code is available at https://github.com/yiyunchen/ESASN.

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .