arXiv:2311.12077v2 Announce Type: replace 
Abstract: Arbitrary-scale image super-resolution employing implicit neural functions has gained significant attention lately due to its capability to upscale images across diverse scales utilizing only a single model. Nevertheless, these methodologies have imposed substantial computational demands as they involve querying every target pixel to a single resource-intensive decoder. In this paper, we introduce a novel and efficient framework, the Mixture-of-Experts Implicit Super-Resolution (MoEISR), which enables super-resolution at arbitrary scales with significantly increased computational efficiency without sacrificing reconstruction quality. MoEISR dynamically allocates the most suitable decoding expert to each pixel using a lightweight mapper module, allowing experts with varying capacities to reconstruct pixels across regions with diverse complexities. Our experiments demonstrate that MoEISR successfully reduces significant amount of floating point operations (FLOPs) while delivering comparable or superior peak signal-to-noise ratio (PSNR).

تم تقديم إطار جديد يسمى Mixture-of-Experts لتحسين دقة الصور (MoEISR)، والذي يهدف إلى تحسين كفاءة تكبير الصور عبر مقاييس عشوائية مع الحفاظ على جودة عالية. هذا مهم لأن الطرق التقليدية تتطلب موارد حسابية كبيرة، مما يجعلها أقل عملية للاستخدام على نطاق واسع. من خلال تحسين العملية، يمكن أن يحدث MoEISR ثورة في كيفية معالجة الصور، مما يسمح بتحسين أسرع وأكثر سهولة للصور عبر تطبيقات متنوعة.

Se ha introducido un nuevo marco llamado Mixture-of-Experts para la super-resolución de imágenes (MoEISR), que busca mejorar la eficiencia del escalado de imágenes a escala arbitraria mientras mantiene una alta calidad. Esto es significativo porque los métodos tradicionales requieren recursos computacionales pesados, lo que los hace menos prácticos para un uso generalizado. Al optimizar el proceso, MoEISR podría revolucionar la forma en que se procesan las imágenes, permitiendo una mejora de imágenes más rápida y accesible en diversas aplicaciones.

Un nouveau cadre appelé Mixture-of-Experts pour la super-résolution d'images (MoEISR) a été introduit, visant à améliorer l'efficacité de l'upscaling d'images à échelle arbitraire tout en maintenant une haute qualité. Cela est important car les méthodes traditionnelles nécessitent des ressources informatiques lourdes, les rendant moins pratiques pour une utilisation généralisée. En optimisant le processus, MoEISR pourrait révolutionner la façon dont les images sont traitées, permettant un meilleur traitement d'images plus rapide et plus accessible dans diverses applications.

A new framework called Mixture-of-Experts for Image Super-Resolution (MoEISR) has been introduced, which aims to enhance the efficiency of arbitrary-scale image upscaling while maintaining high quality. This is significant because traditional methods require heavy computational resources, making them less practical for widespread use. By optimizing the process, MoEISR could revolutionize how images are processed, allowing for faster and more accessible image enhancement across various applications.

Balancing Efficiency and Quality: MoEISR for Arbitrary-Scale Image Super-Resolution

arXiv:2204.13620v5 Announce Type: replace-cross 
Abstract: Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We begin by surveying the development of GANs and popular GAN variants for image-related applications, and then analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Secondly, we compare the performances of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.

استعراض حديث حول الشبكات التنافسية التوليدية (GAN) لتحسين الصورة الفردية (SISR) يسلط الضوء على التقدم في معالجة الصور، مع التركيز على تطبيقات GAN المختلفة وأدائها المقارن على مجموعات البيانات العامة. تؤكد الدراسة على نقص الأدبيات الشاملة التي تلخص هذه التطورات، والتي تعتبر حاسمة لتحسين الصور ذات الدقة المنخفضة.

Una reciente encuesta sobre Redes Generativas Antagónicas (GAN) para la Super-Resolución de Imagen Única (SISR) destaca los avances en el procesamiento de imágenes, centrándose en diversas implementaciones de GAN y su rendimiento comparativo en conjuntos de datos públicos. El estudio enfatiza la falta de literatura completa que resuma estos desarrollos, que son cruciales para mejorar imágenes de baja resolución.

Une récente enquête sur les réseaux antagonistes génératifs (GAN) pour la super-résolution d'image unique (SISR) met en lumière les avancées dans le traitement d'image, en se concentrant sur diverses implémentations de GAN et leur performance comparative sur des ensembles de données publiques. L'étude souligne le manque de littérature complète résumant ces développements, qui sont cruciaux pour améliorer les images de basse résolution.

A recent survey on Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) highlights the advancements in image processing, focusing on various GAN implementations and their comparative performance on public datasets. The study emphasizes the lack of comprehensive literature summarizing these developments, which are crucial for enhancing low-resolution images.

Generative Adversarial Networks for Image Super-Resolution: A Survey

One More Thing in AI – Your Shortcut to AI Mastery

Balancing Efficiency and Quality: MoEISR for Arbitrary-Scale Image Super-Resolution

Was this article worth reading? Share it

One More Thing in AI

LucidQuery AI

Magicley AI

ImgUpscaler AI

4o Image Gen

Bulk Image Generation AI

Ready to build your own newsroom?