2. ESRGAN
The seminal Enhanced SRGAN, introducing Residual‑in‑Residual Dense Blocks (RRDB) and a relativistic discriminator to generate more natural textures.


codes and data will be made publicly available at https://github.com/hasan-rakibul/PC-SRGAN upon acceptance of this paper. [6/6 of https://arxiv.org/abs/2505.06502v1]

arXiv:2505.10589v1 Announce Type: new
Abstract: This study introduces an enhanced approach to video super-resolution by extending ordinary Single-Image Super-Resolution (SISR) Super-Resolution Generative Adversarial Network (SRGAN) structure to [1/6 of https://arxiv.org/abs/2505.10589v1]

Measure compared to conventional methods, even with limited training data (e.g., only 13% of training data required for SRGAN). Beyond SR, PC-SRGAN augments physically meaningful machine learning, incorporating numerically justified time integrators [3/6 of https://arxiv.org/abs/2505.06502v1]

最近、DLSS(Deep Learning Super Sampling)技術が話題ですが、Deep Learning 技術の一つ SRGAN(Super Resolution GAN)を使用して、低解像度画像 144x144（左）から、高解像度画像 576x576 （右）を生成した例。

※1.元画像は LLM が生成したものなので版権問題ありません。
※2.アップスケーリングには、Upscaylを使用。

and advanced quality metrics. These advancements promise reliable and causal machine-learning models in scientific domains. A significant advantage of PC-SRGAN over conventional SR techniques is its physical consistency, which makes it a viable [4/6 of https://arxiv.org/abs/2505.06502v1]

今の私の機械学習への関心に繋がる思い出。

・1996年頃にPS版SimCity 2000でセル オートマトンに感心する。

・2011年頃にOpenOffice Calcでオーディオ波形を微積分で操作して楽しみ、ナイキスト周波数を超えたい欲求が高まる。

・東工大のサブピクセル ブロック マッチングに心躍る。

・2012年のAlexNetやGoogleの猫画像認識ニューラル ネットワークなどのディープ ラーニングの勃興に興奮させられる。

・2015年頃に機械学習系超解像プログラムで遊ぶようになる。

・2017年のSRGANからPCで深層学習を実験するようになる。

今に至る。

handle spatio-temporal data. While SRGAN has proven effective for single-image enhancement, its design does not account for the temporal continuity required in video processing. To address this, a modified framework that incorporates 3D Non-Local [2/6 of https://arxiv.org/abs/2505.10589v1]

Zhu, Zhang, Sfarra, Sarasini, Ding, Ibarra-Castanedo, Maldague: A novel IR-SRGAN assisted super-resolution evaluation of photothermal coherence tomography for impact damage in toughened therm... https://arxiv.org/abs/2509.10894 https://arxiv.org/pdf/2509.10894 https://arxiv.org/html/2509.10894

Md Rakibul Hasan, Pouria Behnoudfar, Dan MacKinlay, Thomas Poulet: PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations https://arxiv.org/abs/2505.06502 https://arxiv.org/pdf/2505.06502 https://arxiv.org/html/2505.06502

UGH. AUGHHHHH. AHHHHHHHHHHHHH. URGH .. ARHHJHH! ARHHKKKKK. SRGAN. ARGHHHHHHKKKKKLK! URGHGH! NAURHHH. RAHHHHHHHHHHHH. AERGHHHHHHH. REAAAAEGH. RSRHHHHH.NR RAGHHHHH. AHHHHHHHHHHHHHHHHHHHHH. GRRER. GRAGHHHH. HARRHHHGH. RARHHHH. AREWHHR. EHHA... ARGHHHHHHHH,N ARGHHHHH RAHGHHHHH RAGHHHHHHHHH. ERAGH. UGHHH

[2025-06-24] 📚 Updates in #GAN

(1) PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations

🔍 More at researchtrend.ai/communities/GAN

surrogate model for time-dependent problems. PC-SRGAN advances scientific machine learning, offering improved accuracy and efficiency for image processing, enhanced process understanding, and broader applications to scientific research. The source [5/6 of https://arxiv.org/abs/2505.06502v1]

scientific applications. Our approach, PC-SRGAN, enhances image resolution while ensuring physical consistency for interpretable simulations. PC-SRGAN significantly improves both the Peak Signal-to-Noise Ratio and the Structural Similarity Index [2/6 of https://arxiv.org/abs/2505.06502v1]

SRGAN の解の一つですよね、30fps 位で処理できるようになれば、使える。

Researchers introduced a lightweight edge‑aware normalized attention (EANA) module for single‑image super‑resolution that improves sharpness and matches or exceeds SRGAN and ESRGAN on benchmarks. https://getnews.me/edge-aware-normalized-attention-improves-single-image-super-resolution/ #srisr #eana

🎧🎶🔊 Apollo Model Revolutionizes High-Quality Audio Restoration www.azoai.com/news/2024091... #ApolloModel #AudioRestoration #GenerativeModel #HighQualityAudio #MusicRestoration #AIInnovation #TechBreakthrough #AudioTech #SRGAN #RealTimeRestoration @arxiv-stat-ml.bsky.social