A Fully Progressive Approach to Single-Image Super-Resolution

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [34]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster

Обработка изображений с помощью Shearlets

В статье дано определение и основные возможности для приложений Shearlets. Рассматривается проблема удаления шума с изображения с по- мощью вейвлетов и Shearlets. Проводится сравнение полученных резуль- татов

Data Upcycling Knowledge Distillation for Image Super-Resolution

Knowledge distillation (KD) emerges as a challenging yet promising technique for compressing deep learning models, characterized by the transmission of extensive learning representations from proficient and computationally intensive teacher models to compact student models. However, only a handful of studies have endeavored to compress the models for single image super-resolution (SISR) through KD, with their effects on student model enhancement remaining marginal. In this paper, we put forth an approach from the perspective of efficient data utilization, namely, the Data Upcycling Knowledge Distillation (DUKD) which facilitates the student model by the prior knowledge teacher provided via upcycled in-domain data derived from their inputs. This upcycling process is realized through two efficient image zooming operations and invertible data augmentations which introduce the label consistency regularization to the field of KD for SISR and substantially boosts student model's generalization. The DUKD, due to its versatility, can be applied across a broad spectrum of teacher-student architectures. Comprehensive experiments across diverse benchmarks demonstrate that our proposed DUKD method significantly outperforms previous art, exemplified by an increase of up to 0.5dB in PSNR over baselines methods, and a 67% parameters reduced RCAN model's performance remaining on par with that of the RCAN teacher model

S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution

Nowadays, deep learning based methods have demonstrated impressive performance on ideal super-resolution (SR) datasets, but most of these methods incur dramatically performance drops when directly applied in real-world SR reconstruction tasks with unpredictable blur kernels. To tackle this issue, blind SR methods are proposed to improve the visual results on random blur kernels, which causes unsatisfactory reconstruction effects on ideal low-resolution images similarly. In this paper, we propose a double-win framework for ideal and blind SR task, named S2R, including a light-weight transformer-based SR model (S2R transformer) and a novel coarse-to-fine training strategy, which can achieve excellent visual results on both ideal and random fuzzy conditions. On algorithm level, S2R transformer smartly combines some efficient and light-weight blocks to enhance the representation ability of extracted features with relatively low number of parameters. For training strategy, a coarse-level learning process is firstly performed to improve the generalization of the network with the help of a large-scale external dataset, and then, a fast fine-tune process is developed to transfer the pre-trained model to real-world SR tasks by mining the internal features of the image. Experimental results show that the proposed S2R outperforms other single-image SR models in ideal SR condition with only 578K parameters. Meanwhile, it can achieve better visual results than regular blind SR models in blind fuzzy conditions with only 10 gradient updates, which improve convergence speed by 300 times, significantly accelerating the transfer-learning process in real-world situations

Remote Sensing Single Image Super-Resolution Benchmarking with Transfer Learning Algorithms

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIn the context of real-world applications like medical imaging systems, tracking, astronomical imaging, navigation, and remote sensing (RS), there is a pressing need to enhance or upscale images with minimal errors. This is particularly critical for tasks such as target detection, image classification, and land use mapping. However, remote sensing images often suffer from limitations in spatial, spectral, radiometric, and temporal resolution due to complex atmospheric conditions and sensor constraints. Additionally, acquiring these images can be expensive and time-consuming. In this study, we propose a Single Image Super-Resolution (SISR) method to address these challenges by upscaling low-quality remote sensing images to higher resolution, enabling a better understanding of these images. We also discuss the specific challenges in remote sensing super-resolution techniques and review various upscaling approaches, while analyzing the impact of other factors like weather conditions, image capture time, and different scene types on the technique’s effectiveness

Annual Report 2011 : Institute for Nuclear Waste Disposal. (KIT Scientific Reports ; 7617)

The R&D at the Institute for Nuclear Waste Disposal, INE, (Institut für Nukleare Entsorgung) of the Karlsruhe Institute of Technology (KIT) focuses on (i) long term safety research for nuclear waste disposal, (ii) immobilization of high level radioactive waste (HLW), (iii) separation of minor actinides from HLW and (iv) radiation protection

Symmetry reduction and shape effects in concave chiral plasmonic structures

Chiral metamaterials have shown a number of interesting properties which result from the interaction of the chiral near-field they produce with light and matter. We investigate the influence of structural imperfections on the plasmonic properties of a chiral gold “gammadion”, using electron energy loss spectroscopy to directly inform simulations of realistic, imperfect structures. Unlike structures of simple convex geometry, the lowest energy modes of the ideal concave gammadion have a quadrupole and dipole character, with the mode energies determined by the nature of electrostatic coupling between the gammadion arms. These modes are strongly affected by structural imperfections that are inherent to the material properties and lithographic patterning. Even subwavelength-scale imperfections reduce the symmetry, lift mode degeneracies convert dark modes into bright ones and significantly alter the mode energy, its near-field strength, and chirality. Such effects will be common to a number of multitipped concave structures currently being investigated for the chiral fields they support

Blind Image Super-resolution with Rich Texture-Aware Codebooks

Blind super-resolution (BSR) methods based on high-resolution (HR) reconstruction codebooks have achieved promising results in recent years. However, we find that a codebook based on HR reconstruction may not effectively capture the complex correlations between low-resolution (LR) and HR images. In detail, multiple HR images may produce similar LR versions due to complex blind degradations, causing the HR-dependent only codebooks having limited texture diversity when faced with confusing LR inputs. To alleviate this problem, we propose the Rich Texture-aware Codebook-based Network (RTCNet), which consists of the Degradation-robust Texture Prior Module (DTPM) and the Patch-aware Texture Prior Module (PTPM). DTPM effectively mines the cross-resolution correlation of textures between LR and HR images by exploiting the cross-resolution correspondence of textures. PTPM uses patch-wise semantic pre-training to correct the misperception of texture similarity in the high-level semantic regularization. By taking advantage of this, RTCNet effectively gets rid of the misalignment of confusing textures between HR and LR in the BSR scenarios. Experiments show that RTCNet outperforms state-of-the-art methods on various benchmarks by up to 0.16 ~ 0.46dB