Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene. SRR is particularly valuable, if it is infeasible to acquire images at desired resolution, but many images of the same scene are available at lower resolution---this is inherent to a variety of remote sensing scenarios. Recently, we have witnessed substantial improvement in single-image SRR attributed to the use of deep neural networks for learning the relation between low and high resolution. Importantly, deep learning has not been exploited for multiple-image SRR, which benefits from information fusion and in general allows for achieving higher reconstruction accuracy. In this letter, we introduce a new method which combines the advantages of multiple-image fusion with learning the low-to-high resolution mapping using deep networks. The reported experimental results indicate that our algorithm outperforms the state-of-the-art SRR methods, including these that operate from a single image, as well as those that perform multiple-image fusion.
Recently, we proposed the EvoIM method @cite_10 @cite_19 , which employs a genetic algorithm to optimize the hyper-parameters that control the IM used in FRSR @cite_13 , and to evolve the convolution kernels instead of the Gaussian blur used in FRSR. We showed that the reconstruction process can be effectively adapted to different imaging conditions---in particular, we used Sentinel-2 images at original resolution as LR inputs, and compared the reconstruction outcome with SPOT images presenting the same region.
@cite_13 @cite_19 @cite_10
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Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually. Super-resolution reconstruction (SRR) allows for producing a high-resolution (HR) image from a set of low-resolution (LR) observations. The majority of existing methods require tuning a number of hyper-parameters which control the reconstruction process and configure the imaging model that is supposed to reflect the relation between high and low resolution. In this paper, we demonstrate that the reconstruction process is very sensitive to the actual relation between LR and HR images, and we argue that this is a substantial obstacle in deploying SRR in practice. We propose to search the hyper-parameter space using a genetic algorithm (GA), thus adapting to the actual relation between LR and HR, which has not been reported in the literature so far. The results of our extensive experimental study clearly indicate that our GA improves the capacities of SRR. Importantly, the GA converges to different values of the hyper-parameters depending on the applied degradation procedure, which is confirmed using statistical tests. This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality. Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods.