Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.
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Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.
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The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.
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Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.
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我们为移动设备提出了一个轻巧的单图超分辨率网络,名为XCAT。XCAT引入了具有交叉串联(HXBLOCK)的异质群卷积块。输入通道向组卷积块的异质拆分减少了操作数量,交叉串联允许在级联HXBlocks的中间输入张量之间进行信息流。HXBlocks内部的交叉串联也可以避免使用更昂贵的操作,例如1x1卷积。为了进一步预见昂贵的张量副本操作,XCAT利用不可训练的卷积内核来应用采样操作。XCAT考虑了整数量化的设计,还利用了几种技术,例如基于强度的数据增强。Integer的XCAT量化XCAT可在320ms的Mali-G71 MP2 GPU上实时运行,以及适用于实时应用的30ms(NCHW)和8.8ms(NHWC)的Synaptics Dolphin NPU。
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本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率,轨迹〜2靶向压缩视频的超分辨率。在轨道1中,我们使用流行的数据集DIV2K作为培训,验证和测试集。在轨道2中,我们提出了LDV 3.0数据集,其中包含365个视频,包括LDV 2.0数据集(335个视频)和30个其他视频。在这一挑战中,有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。
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基于深度学习的单图像超分辨率(SISR)方法引起了人们的关注,并在现代高级GPU上取得了巨大的成功。但是,大多数最先进的方法都需要大量参数,记忆和计算资源,这些参数通常会显示在当前移动设备CPU/NPU上时显示出较低的推理时间。在本文中,我们提出了一个简单的普通卷积网络,该网络具有快速最近的卷积模块(NCNET),该模块对NPU友好,可以实时执行可靠的超级分辨率。提出的最近的卷积具有与最近的UP采样相同的性能,但更快,更适合Android NNAPI。我们的模型可以很容易地在具有8位量化的移动设备上部署,并且与所有主要的移动AI加速器完全兼容。此外,我们对移动设备上的不同张量操作进行了全面的实验,以说明网络体系结构的效率。我们的NCNET在DIV2K 3X数据集上进行了训练和验证,并且与其他有效的SR方法的比较表明,NCNET可以实现高保真SR结果,同时使用更少的推理时间。我们的代码和预估计的模型可在\ url {https://github.com/algolzw/ncnet}上公开获得。
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This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results.A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∼ 100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.
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The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations. While deep learning-based approaches can efficiently solve this problem, their computational requirements usually remain too large for high-resolution on-device image processing. To address this limitation, we propose a novel PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being able to process RAW 12MP photos directly on mobile phones under 1.5 second and producing high perceptual photo quality. To train and to evaluate the performance of the proposed solution, we use the real-world Fujifilm UltraISP dataset consisting on thousands of RAW-RGB image pairs captured with a professional medium-format 102MP Fujifilm camera and a popular Sony mobile camera sensor. The results demonstrate that the PyNET-V2 Mobile model can substantially surpass the quality of tradition ISP pipelines, while outperforming the previously introduced neural network-based solutions designed for fast image processing. Furthermore, we show that the proposed architecture is also compatible with the latest mobile AI accelerators such as NPUs or APUs that can be used to further reduce the latency of the model to as little as 0.5 second. The dataset, code and pre-trained models used in this paper are available on the project website: https://github.com/gmalivenko/PyNET-v2
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随着移动设备的普及,例如智能手机和可穿戴设备,更轻,更快的型号对于应用视频超级分辨率至关重要。但是,大多数以前的轻型模型倾向于集中于减少台式GPU模型推断的范围,这在当前的移动设备中可能不会节能。在本文中,我们提出了极端低功率超级分辨率(ELSR)网络,该网络仅在移动设备中消耗少量的能量。采用预训练和填充方法来提高极小模型的性能。广泛的实验表明,我们的方法在恢复质量和功耗之间取得了良好的平衡。最后,我们在目标总经理Dimenty 9000 PlantForm上,PSNR 27.34 dB和功率为0.09 w/30fps的竞争分数为90.9,在移动AI&AIM 2022实时视频超级分辨率挑战中排名第一。
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本文回顾了关于压缩视频质量增强质量的第一个NTIRE挑战,重点是拟议的方法和结果。在此挑战中,采用了新的大型不同视频(LDV)数据集。挑战有三个曲目。Track 1和2的目标是增强HEVC在固定QP上压缩的视频,而Track 3旨在增强X265压缩的视频,以固定的位速率压缩。此外,轨道1和3的质量提高了提高保真度(PSNR)的目标,以及提高感知质量的2个目标。这三个曲目完全吸引了482个注册。在测试阶段,分别提交了12个团队,8支球队和11支球队,分别提交了轨道1、2和3的最终结果。拟议的方法和解决方案衡量视频质量增强的最先进。挑战的首页:https://github.com/renyang-home/ntire21_venh
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随着卷积神经网络最近的大规模发展,已经提出了用于边缘设备上实用部署的大量基于CNN的显着图像超分辨率方法。但是,大多数现有方法都集中在一个特定方面:网络或损失设计,这导致难以最大程度地减少模型大小。为了解决这个问题,我们得出结论,设计,架构搜索和损失设计,以获得更有效的SR结构。在本文中,我们提出了一个名为EFDN的边缘增强功能蒸馏网络,以保留在约束资源下的高频信息。详细说明,我们基于现有的重新处理方法构建了一个边缘增强卷积块。同时,我们提出了边缘增强的梯度损失,以校准重新分配的路径训练。实验结果表明,我们的边缘增强策略可以保持边缘并显着提高最终恢复质量。代码可在https://github.com/icandle/efdn上找到。
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随着移动平台上对计算摄影和成像的需求不断增长,在相机系统中开发和集成了高级图像传感器与新型算法的发展。但是,缺乏用于研究的高质量数据以及从行业和学术界进行深入交流的难得的机会限制了移动智能摄影和成像(MIPI)的发展。为了弥合差距,我们引入了第一个MIPI挑战,其中包括五个专注于新型图像传感器和成像算法的曲目。在本文中,引入了QUAD Remosaic和Denoise,这是五个曲目之一,在完全分辨率上进行了四QFA插值向拜耳进行插值。为参与者提供了一个新的数据集,包括70(培训)和15个(验证)高品质四边形和拜耳对的场景。此外,对于每个场景,在0dB,24dB和42dB上提供了不同噪声水平的四边形。所有数据均在室外和室内条件下使用四边形传感器捕获。最终结果使用客观指标,包括PSNR,SSIM,LPIPS和KLD。本文提供了此挑战中所有模型的详细描述。有关此挑战的更多详细信息以及数据集的链接,请访问https://github.com/mipi-challenge/mipi2022。
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随着移动平台上对计算摄影和成像的需求不断增长,在相机系统中开发和集成了高级图像传感器与新型算法的发展。但是,缺乏用于研究的高质量数据以及从行业和学术界进行深入交流的难得的机会限制了移动智能摄影和成像(MIPI)的发展。为了弥合差距,我们介绍了第一个MIPI挑战,包括五个曲目,这些曲目着重于新型图像传感器和成像算法。在本文中,我们总结并审查了MIPI 2022上的分配摄像头(UDC)图像恢复轨道。总共,成功注册了167名参与者,并在最终测试阶段提交了19个团队。在这项挑战中开发的解决方案在播放摄像头映像修复局上实现了最新的性能。本文提供了此挑战中所有模型的详细描述。有关此挑战的更多详细信息以及数据集的链接,请访问https://github.com/mipi-challenge/mipi2022。
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单像超分辨率可以在需要可靠的视觉流以监视任务,处理远程操作或研究相关视觉细节的环境中支持机器人任务。在这项工作中,我们为实时超级分辨率提出了一个有效的生成对抗网络模型。我们采用了原始SRGAN的量身定制体系结构和模型量化,以提高CPU和Edge TPU设备上的执行,最多达到200 fps的推断。我们通过将其知识提炼成较小版本的网络,进一步优化我们的模型,并与标准培训方法相比获得显着的改进。我们的实验表明,与较重的最新模型相比,我们的快速和轻量级模型可保持相当令人满意的图像质量。最后,我们对图像传输进行带宽降解的实验,以突出提出的移动机器人应用系统的优势。
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随着深度学习(DL)的出现,超分辨率(SR)也已成为一个蓬勃发展的研究领域。然而,尽管结果有希望,但该领域仍然面临需要进一步研究的挑战,例如,允许灵活地采样,更有效的损失功能和更好的评估指标。我们根据最近的进步来回顾SR的域,并检查最新模型,例如扩散(DDPM)和基于变压器的SR模型。我们对SR中使用的当代策略进行了批判性讨论,并确定了有前途但未开发的研究方向。我们通过纳入该领域的最新发展,例如不确定性驱动的损失,小波网络,神经体系结构搜索,新颖的归一化方法和最新评估技术来补充先前的调查。我们还为整章中的模型和方法提供了几种可视化,以促进对该领域趋势的全球理解。最终,这篇综述旨在帮助研究人员推动DL应用于SR的界限。
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Recently, great progress has been made in single-image super-resolution (SISR) based on deep learning technology. However, the existing methods usually require a large computational cost. Meanwhile, the activation function will cause some features of the intermediate layer to be lost. Therefore, it is a challenge to make the model lightweight while reducing the impact of intermediate feature loss on the reconstruction quality. In this paper, we propose a Feature Interaction Weighted Hybrid Network (FIWHN) to alleviate the above problem. Specifically, FIWHN consists of a series of novel Wide-residual Distillation Interaction Blocks (WDIB) as the backbone, where every third WDIBs form a Feature shuffle Weighted Group (FSWG) by mutual information mixing and fusion. In addition, to mitigate the adverse effects of intermediate feature loss on the reconstruction results, we introduced a well-designed Wide Convolutional Residual Weighting (WCRW) and Wide Identical Residual Weighting (WIRW) units in WDIB, and effectively cross-fused features of different finenesses through a Wide-residual Distillation Connection (WRDC) framework and a Self-Calibrating Fusion (SCF) unit. Finally, to complement the global features lacking in the CNN model, we introduced the Transformer into our model and explored a new way of combining the CNN and Transformer. Extensive quantitative and qualitative experiments on low-level and high-level tasks show that our proposed FIWHN can achieve a good balance between performance and efficiency, and is more conducive to downstream tasks to solve problems in low-pixel scenarios.
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尽管具有卷积神经网络(CNN)的图像超分辨率(SR)的突破性进步,但由于SR网络的计算复杂性很高,SR尚未享受无处不在的应用。量化是解决此问题的有前途方法之一。但是,现有的方法无法量化低于8位的位宽度的SR模型,由于固定的位宽度量化量的严重精度损失。在这项工作中,为了实现高平均比重减少,准确性损失较低,我们建议针对SR网络的新颖的内容感知动态量化(CADYQ)方法,该方法将最佳位置分配给本地区域和层,并根据输入的本地内容适应。图片。为此,引入了一个可训练的位选择器模块,以确定每一层和给定的本地图像补丁的适当位宽度和量化水平。该模块受量化灵敏度的控制,该量化通过使用贴片的图像梯度的平均幅度和层的输入特征的标准偏差来估计。拟议的量化管道已在各种SR网络上进行了测试,并对几个标准基准进行了广泛评估。计算复杂性和升高恢复精度的显着降低清楚地表明了SR提出的CADYQ框架的有效性。代码可从https://github.com/cheeun/cadyq获得。
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In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to realworld applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.
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Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge [26].
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