Blind Image Super-Resolution (BSR) tackles the challenge of enhancing image resolution that has been degraded by unknown kernels. Although existing BSR models have achieved remarkable results, they often demand significant computational resources to manage various degradation kernels. Recent studies have utilized large models with parameter counts ranging from 4M to 20M, resulting in computational costs exceeding 200G Multi-Adds. In this paper, we introduce a blind super-resolution method, which is the first lightweight non-iterative approach for BSR. By leveraging the connection between degradation kernel shapes and the frequency-domain characteristics of low-resolution images, we simplify the kernel estimation process, thereby reducing the overall model complexity. Additionally, we employ a constrained least squares approach to refine the low-resolution image using the estimated kernel, which serves as the input to the hierarchical Transformer blocks. Our approach delivers competitive performance while requiring only 1.7M parameters and 2G Multi-Adds. Experimental results demonstrate that our method achieves comparable results with significantly smaller network size

Training deep neural networks has become increasingly demanding, requiring large datasets and significant computational resources, especially as model complexity advances. Data distillation methods, which aim to improve data efficiency, have emerged as promising solutions to this challenge. In the field of single image super-resolution (SISR), the reliance on large training datasets highlights the importance of these techniques. Recently, a generative adversarial network (GAN) inversion-based data distillation framework for SR was proposed, showing potential for better data utilization. However, the current method depends heavily on pre-trained SR networks and class-specific information, limiting its generalizability and applicability. To address these issues, we introduce a new data distillation approach for image SR that does not need class labels or pre-trained SR models. In particular, we first extract high- gradient patches and categorize images based on CLIP features, then fine-tune a diffusion model on the selected patches to learn their distribution and synthesize distilled training images. Experimental results show that our method achieves state-of-the-art performance while using significantly less training data and requiring less computational time. Specifically, when we train a baseline Transformer model for SR with only 0.68% of the original dataset, the performance drop is just 0.3 dB. In this case, diffusion model fine-tuning takes 4 hours, and SR model training completes within 1 hour, much shorter than the 11-hour training time with the full dataset.

In this paper, we propose a new user-plane (UP) network architecture for 6G cellular networks, where there is no distinction between radio access network (RAN) and core network (CN). We define a new network function (NF), called merged-UP (M-UP), where the UP functions of both RAN and CN sides are merged, thus enabling efficient protocol operations. We also propose an IP flow-based radio quality-of-service (QoS) control scheme to support 6G services with diverse QoS requirements. Based on our network testbed system, we show that the proposed architecture improves the system performance in terms of packet processing load and maximum throughput.

Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. Despite their remarkable performance based on the external dataset, they cannot exploit internal information within a specific image. Another problem is that they are applicable only to the specific condition of data that they are supervised. For instance, the low-resolution (LR) image should be a "bicubic" downsampled noise-free image from a high-resolution (HR) one. To address both issues, zero-shot super-resolution (ZSSR) has been proposed for flexible internal learning. However, they require thousands of gradient updates, i.e., long inference time. In this paper, we present Meta-Transfer Learning for Zero-Shot Super-Resolution (MZSR), which leverages ZSSR. Precisely, it is based on finding a generic initial parameter that is suitable for internal learning. Thus, we can exploit both external and internal information, where one single gradient update can yield quite considerable results. With our method, the network can quickly adapt to a given image condition. In this respect, our method can be applied to a large spectrum of image conditions within a fast adaptation process.