Single image super-resolution using a deep encoder–decoder symmetrical network with iterative back projection

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.

Blind super-resolution (SR) of blurry and noisy low-resolution (LR) images is still a challenging problem in single image super-resolution (SISR). The performance of most existing convolutional neural network (CNN)-based models is inevitably degraded when LR images are corrupted by both blur and noise. For those blind SR methods based on kernel estimation, accurate estimation is barely attained under complex degradations and this gives rise to poor-quality results. To address these problems, we propose a deep progressive network under a probabilistic framework and a novel up-sampling method for blind super-resolution with multiple degradations, which effectively utilizes image priors across scales. Experimental results show that the proposed method achieves promising performance on images with multiple degradations.

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.

Streaming low bitrate while preserving high-quality video content is a crucial topic in multimedia and video surveillance. In this work, we explore the problem of spatially and temporally reconstructing high-resolution (HR) frames from a high frame-rate low-resolution (LR) sequence and a few temporally subsampled HR frames. The targeted problem is essentially different from the problems handled by typical super-resolution (SR) methods such as single-image SR and video SR, which attempt to reconstruct HR images using only LR images. To tackle the targeted problem, we propose a deep quality transfer network, based on the convolutional neural network (CNN), which consists of modules including generation and selection of HR pixel candidates, fusion with LR input, residual learning and bidirectional architecture. The proposed CNN model has real-time performance at inference stage. The empirical studies have verified the generality of the proposed CNN model showing significant quality gains for video enhancement.

Image restoration, such as single image super-resolution (SISR), is a long-established low-level vision issue that intends to regenerate high-resolution (HR) images from low-resolution (LR) input counterparts. While state-of-the-art image super-resolution models are based on the well-known convolutional neural network (CNN), many self-attention-based or transformer-based experiment attempts have been conducted. They have shown promising performance on vision problems. A powerful baseline model based on the swin transformer adopts the shifted window approach. It enhances the capability by restricting the model to compute the self-attention function only on non-superimpose local windows while enabling cross-window relations. However, the architecture design is manually fixed. Therefore, the results are not achieving optimal performance. This work presents a genetic algorithm-based deep multi-route self-attention network for single image super-resolution (GA-MRSA). The genetic algorithm (GA) is introduced to discover the more suitable number of filters and layers. Experimental results demonstrate that the proposed optimization technique can produce an SR image with a maximum progressive PSNR of 0.14 dB and an average of 0.06 dB in the testing datasets compared to the state-of-the-art.

Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and high-resolution (HR) outputs. These existing SR methods do not take into account the image observation (physical) model and thus require a large number of network's trainable parameters with a great volume of training data. To address these issues, we propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Extensive experimental results on various super-resolution benchmarks demonstrate that our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.

High resolution images obtained from single image super resolution often suffer from excessive smoothness which blurs edges and gives smooth areas an un-natural texture. This paper proposes a super resolution method that combines wavelet super resolution and an edge enhancing back-projection filter to produce a super resolution image with sharp edges and natural texture. The low resolution image is first up-sampled using the discrete wavelet transform. High frequency sub-bands are derived from DWT of a bicubic interpolated low resolution image. The up-sampled image is then subjected to iterative back projection using a back-projection filter based on the Laplacian of Gaussian. The proposed method is tested on RGB colour images. It is found to produce better results than bicubic interpolation and selected edge directed interpolation methods in terms of PSNR and SSIM. It is also found to approximate the edge sharpness of edge directed interpolation while giving a more natural texture in smooth areas. The proposed method also eliminates spurious colours that have been observed in most edge-directed methods.

Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples. Most existing deep networks for SR produce high quality results when training data is abundant. However, their performance degrades sharply when training is limited. We propose to regularize deep structures with prior knowledge about the images so that they can capture more structural information from the same limited data. In particular, we incorporate in a tractable fashion within the CNN framework, natural image priors which have shown to have much recent success in imaging and vision inverse problems. Experimental results show that the proposed deep network with natural image priors is particularly effective in training starved regimes.

In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results. Thanks to the powerful representation capabilities of the deep networks, numerous previous ways can learn the complex non-linear mapping between low-resolution (LR) image patches and their high-resolution (HR) versions. However, excessive convolutions will limit the application of super-resolution technology in low computing power devices. Besides, super-resolution of any arbitrary scale factor is a critical issue in practical applications, which has not been well solved in the previous approaches. To address these issues, we propose a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multi-distillation blocks (IMDB), which contains distillation and selective fusion parts. Specifically, the distillation module extracts hierarchical features step-by-step, and fusion module aggregates them according to the importance of candidate features, which is evaluated by the proposed contrast-aware channel attention mechanism. To process real images with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve block-wise image patches using the same well-trained model. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, memory footprint, and inference time. Code is available at \url{https://github.com/Zheng222/IMDN}.

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.

The single image super resolution recovers missing high resolution details so as to reconstruct a high resolution image from a single low resolution image. This paper proposes a novel directionally adaptive, learning-based, single image super resolution method using multiple direction wavelet transform, called directionlets. Here, critically sampled directionlets are used to capture directional features effectively and to extract edge information along different directions from a set of available high resolution images. This information is used as the training set for super resolving a low resolution input image. The directionlet coefficients at finer scales of its high resolution image are learned locally from this training set and the inverse directionlet transform recovers the super resolved high resolution image. The simulation results showed that the proposed directionlet approach outperforms standard interpolation techniques like cubic spline interpolation as well as standard wavelet-based learning, both visually and in terms of the mean squared error (MSE) values. The SNR scores for cubic spline interpolation, wavelet and directionlet method are 13.6998 dB, 23.8324 dB and 30.8654 dB respectively for Barbara.

Traditional works have shown that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Make full use of these multi-scale information can improve the image restoration performance. However, the current proposed deep learning based restoration methods do not take the multi-scale information into account. In this paper, we propose a dilated convolution based inception module to learn multi-scale information and design a deep network for single image super-resolution. Different dilated convolution learns different scale feature, then the inception module concatenates all these features to fuse multi-scale information. In order to increase the reception field of our network to catch more contextual information, we cascade multiple inception modules to constitute a deep network to conduct single image super-resolution. With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance. Experimental results show that our proposed method outperforms many state-of-the-art single image super-resolution methods.

Adversarially trained deep neural networks have significantly improved performance of single image super resolution, by hallucinating photorealistic local textures, thereby greatly reducing the perception difference between a real high resolution image and its super resolved (SR) counterpart. However, application to medical imaging requires preservation of diagnostically relevant features while refraining from introducing any diagnostically confusing artifacts. We propose using a deep convolutional super resolution network (SRNet) trained for (i) minimising reconstruction loss between the real and SR images, and (ii) maximally confusing learned relativistic visual Turing test (rVTT) networks to discriminate between (a) pair of real and SR images (T1) and (b) pair of patches in real and SR selected from region of interest (T2). The adversarial loss of T1 and T2 while backpropagated through SRNet helps it learn to reconstruct pathorealism in the regions of interest such as white blood cells (WBC) in peripheral blood smears or epithelial cells in histopathology of cancerous biopsy tissues, which are experimentally demonstrated here. Experiments performed for measuring signal distortion loss using peak signal to noise ratio (pSNR) and structural similarity (SSIM) with variation of SR scale factors, impact of rVTT adversarial losses, and impact on reporting using SR on a commercially available artificial intelligence (AI) digital pathology system substantiate our claims.

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.

To improve the spatial resolution of video, a superresolution reconstruction method based on a sliding window is proposed utilizing the movement information between frames in the low-resolution video. We propose a registration algorithm based on a four-parameter transformation model through Taylor series expansion, using an iterative solving method as well as the Gaussian pyramid image model to estimate the movement parameters from coarseness to fine. Superresolution frames are reconstructed using an iterative back projection (IBP) algorithm. We also present the suitable length of the sliding window and the reasonable iteration number of the IBP algorithm in the video superresolution reconstruction. Our algorithm is compared to other algorithms on simulated images and actual color videos. Both show that our registration algorithm achieves higher subpixel accuracy than other algorithms, even in the case of large movements, and that the reconstructed video has better visual effects and stronger resolution ability. It can be extensively applied to the superresolution reconstruction of video sequences in which the frames are different from each other mainly by translation and rotation.