Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature have been focusing on the Single-Image Super-Resolution problem so far. At present, satellite based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the multi-image super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for single or multi-image super-resolution, have demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-Image Super-Resolution for remote sensing applications

Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution

In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural categories, most existing deep learning models for image super-resolution tend to exploit an indiscriminate scheme for processing one whole image. Inspired by the human cognitive mechanism, we propose a multiple convolutional neural network framework trained based on different textural clusters of image local patches. To this end, we commence by grouping patches into K clusters via K-means, which enables each cluster center to encode image priors of a certain texture category. We then train K convolutional neural networks for super-resolution based on the K clusters of patches separately, such that the multiple convolutional neural networks comprehensively capture the patch textural variability. Furthermore, each convolutional neural network characterizes one specific texture category and is used for restoring patches belonging to the cluster. In this way, the texture variation within a whole image is characterized by assigning local patches to their closest cluster centers, and the super-resolution of each local patch is conducted via the convolutional neural network trained by its cluster. Our proposed framework not only exploits the deep learning capability of convolutional neural networks but also adapts them to depict texture diversities for super-resolution. Experimental super-resolution evaluations on benchmark image datasets validate that our framework achieves state-of-the-art performance in terms of peak signal-to-noise ratio and structural similarity. Our multiple convolutional neural network framework provides an enhanced image super-resolution strategy over existing single-mode deep learning models

NLCUnet: Single-Image Super-Resolution Network with Hairline Details

Pursuing the precise details of super-resolution images is challenging for single-image super-resolution tasks. This paper presents a single-image super-resolution network with hairline details (termed NLCUnet), including three core designs. Specifically, a non-local attention mechanism is first introduced to restore local pieces by learning from the whole image region. Then, we find that the blur kernel trained by the existing work is unnecessary. Based on this finding, we create a new network architecture by integrating depth-wise convolution with channel attention without the blur kernel estimation, resulting in a performance improvement instead. Finally, to make the cropped region contain as much semantic information as possible, we propose a random 64$\times$64 crop inside the central 512$\times$512 crop instead of a direct random crop inside the whole image of 2K size. Numerous experiments conducted on the benchmark DF2K dataset demonstrate that our NLCUnet performs better than the state-of-the-art in terms of the PSNR and SSIM metrics and yields visually favorable hairline details.Comment: 6 pages,5 figure

Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels

The deep learning technique was used to increase the performance of single image super-resolution (SISR). However, most existing CNN-based SISR approaches primarily focus on establishing deeper or larger networks to extract more significant high-level features. Usually, the pixel-level loss between the target high-resolution image and the estimated image is used, but the neighbor relations between pixels in the image are seldom used. On the other hand, according to observations, a pixel's neighbor relationship contains rich information about the spatial structure, local context, and structural knowledge. Based on this fact, in this paper, we utilize pixel's neighbor relationships in a different perspective, and we propose the differences of neighboring pixels to regularize the CNN by constructing a graph from the estimated image and the ground-truth image. The proposed method outperforms the state-of-the-art methods in terms of quantitative and qualitative evaluation of the benchmark datasets. Keywords: Super-resolution, Convolutional Neural Networks, Deep Learnin

Video and Image Super-Resolution via Deep Learning with Attention Mechanism

Image demosaicing, image super-resolution and video super-resolution are three important tasks in color imaging pipeline. Demosaicing deals with the recovery of missing color information and generation of full-resolution color images from so-called Color filter Array (CFA) such as Bayer pattern. Image super-resolution aims at increasing the spatial resolution and enhance important structures (e.g., edges and textures) in super-resolved images. Both spatial and temporal dependency are important to the task of video super-resolution, which has received increasingly more attention in recent years. Traditional solutions to these three low-level vision tasks lack generalization capability especially for real-world data. Recently, deep learning methods have achieved great success in vision problems including image demosaicing and image/video super-resolution. Conceptually similar to adaptation in model-based approaches, attention has received increasing more usage in deep learning recently. As a tool to reallocate limited computational resources based on the importance of informative components, attention mechanism which includes channel attention, spatial attention, non-local attention, etc. has found successful applications in both highlevel and low-level vision tasks. However, to the best of our knowledge, 1) most approaches independently studied super-resolution and demosaicing; little is known about the potential benefit of formulating a joint demosaicing and super-resolution (JDSR) problem; 2) attention mechanism has not been studied for spectral channels of color images in the open literature; 3) current approaches for video super-resolution implement deformable convolution based frame alignment methods and naive spatial attention mechanism. How to exploit attention mechanism in spectral and temporal domains sets up the stage for the research in this dissertation. In this dissertation, we conduct a systematic study about those two issues and make the following contributions: 1) we propose a spatial color attention network (SCAN) designed to jointly exploit the spatial and spectral dependency within color images for single image super-resolution (SISR) problem. We present a spatial color attention module that calibrates important color information for individual color components from output feature maps of residual groups. Experimental results have shown that SCAN has achieved superior performance in terms of both subjective and objective qualities on the NTIRE2019 dataset; 2) we propose two competing end-to-end joint optimization solutions to the JDSR problem: Densely-Connected Squeeze-and-Excitation Residual Network (DSERN) vs. Residual-Dense Squeeze-and-Excitation Network (RDSEN). Experimental results have shown that an enhanced design RDSEN can significantly improve both subjective and objective performance over DSERN; 3) we propose a novel deep learning based framework, Deformable Kernel Spatial Attention Network (DKSAN) to super-resolve videos with a scale factor as large as 16 (the extreme SR situation). Thanks to newly designed Deformable Kernel Convolution Alignment (DKC Align) and Deformable Kernel Spatial Attention (DKSA) modules, DKSAN can get both better subjective and objective results when compared with the existing state-of-the-art approach enhanced deformable convolutional network (EDVR)