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
January 6, 2025 at 8:39 AM
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
Kurmi, Sappo, Srinivas, Kandemir, Viswanathan, Zu: PGDM-MRSRGAN: Physics-Guided Degradation Model with an SRGAN Framework for Magnetic Resonance Image Super-Resolution: Applications in Low-Fi... https://arxiv.org/abs/2609.30431 https://arxiv.org/pdf/2609.30431 https://arxiv.org/html/2609.30431
September 28, 2026 at 6:45 AM
Kurmi, Sappo, Srinivas, Kandemir, Viswanathan, Zu: PGDM-MRSRGAN: Physics-Guided Degradation Model with an SRGAN Framework for Magnetic Resonance Image Super-Resolution: Applications in Low-Fi... https://arxiv.org/abs/2609.30431 https://arxiv.org/pdf/2609.30431 https://arxiv.org/html/2609.30431
In 2019, "how did you think this wouldn't work" was a good question to ask about someone who'd gotten StyleGAN or SRGAN to run on their local machine, but with all these chat-interface AI models that manipulate images, the median user has *no* mental model - it's just text-to-magic-images for them.
February 3, 2026 at 10:08 PM
In 2019, "how did you think this wouldn't work" was a good question to ask about someone who'd gotten StyleGAN or SRGAN to run on their local machine, but with all these chat-interface AI models that manipulate images, the median user has *no* mental model - it's just text-to-magic-images for them.
🎧🎶🔊 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
Apollo Model Revolutionizes High-Quality Audio Restoration
Apollo, a generative model, introduces a new approach to high-sample-rate audio restoration, outperforming SR-GAN in restoring compressed audio with higher quality and efficiency. Its innovative band-...
www.azoai.com
September 20, 2024 at 2:15 AM
🎧🎶🔊 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
A new AI review! IBM/MAX-Image-Resolution-Enhancer ⭐3.2/5.0
IBM/MAX-Image-Resolution-Enhancer is a well-packaged “model-as-a-microservice” repository that deploys an SRGAN-based 4× image super-resolution model behind a REST API.
https://gitrated.com/IBM/MAX-Image-Resolution-Enhancer
IBM/MAX-Image-Resolution-Enhancer is a well-packaged “model-as-a-microservice” repository that deploys an SRGAN-based 4× image super-resolution model behind a REST API.
https://gitrated.com/IBM/MAX-Image-Resolution-Enhancer
March 19, 2026 at 7:36 AM
A new AI review! IBM/MAX-Image-Resolution-Enhancer ⭐3.2/5.0
IBM/MAX-Image-Resolution-Enhancer is a well-packaged “model-as-a-microservice” repository that deploys an SRGAN-based 4× image super-resolution model behind a REST API.
https://gitrated.com/IBM/MAX-Image-Resolution-Enhancer
IBM/MAX-Image-Resolution-Enhancer is a well-packaged “model-as-a-microservice” repository that deploys an SRGAN-based 4× image super-resolution model behind a REST API.
https://gitrated.com/IBM/MAX-Image-Resolution-Enhancer
How is ESRGAN for scaling?
arxiv.org/abs/1809.00219
It seemed to look the best from the image gallery at en.wikipedia.org/wiki/Compari...
Throwing a neural network in the mix is a bit long in the tooth; I expected something more like Lanczos resampling or maybe wavelet affairs.
arxiv.org/abs/1809.00219
It seemed to look the best from the image gallery at en.wikipedia.org/wiki/Compari...
Throwing a neural network in the mix is a bit long in the tooth; I expected something more like Lanczos resampling or maybe wavelet affairs.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details a...
arxiv.org
November 1, 2025 at 8:29 PM
How is ESRGAN for scaling?
arxiv.org/abs/1809.00219
It seemed to look the best from the image gallery at en.wikipedia.org/wiki/Compari...
Throwing a neural network in the mix is a bit long in the tooth; I expected something more like Lanczos resampling or maybe wavelet affairs.
arxiv.org/abs/1809.00219
It seemed to look the best from the image gallery at en.wikipedia.org/wiki/Compari...
Throwing a neural network in the mix is a bit long in the tooth; I expected something more like Lanczos resampling or maybe wavelet affairs.
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
September 16, 2025 at 6:46 AM
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
Navaro-Avi\~na, Merin-Martinez, Mendez-Vazquez, Rodriguez-Tello: SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources https://arxiv.org/abs/2605.01459 https://arxiv.org/pdf/2605.01459 https://arxiv.org/html/2605.01459
May 5, 2026 at 6:40 AM
Navaro-Avi\~na, Merin-Martinez, Mendez-Vazquez, Rodriguez-Tello: SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources https://arxiv.org/abs/2605.01459 https://arxiv.org/pdf/2605.01459 https://arxiv.org/html/2605.01459
Simon Donike, Cesar Aybar, Julio Contreras, Luis G\'omez-Chova: OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data https://arxiv.org/abs/2511.10461 https://arxiv.org/pdf/2511.10461 https://arxiv.org/html/2511.10461
November 14, 2025 at 6:31 AM
Simon Donike, Cesar Aybar, Julio Contreras, Luis G\'omez-Chova: OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data https://arxiv.org/abs/2511.10461 https://arxiv.org/pdf/2511.10461 https://arxiv.org/html/2511.10461
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]
May 19, 2025 at 5:56 AM
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]
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]
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]
May 19, 2025 at 5:56 AM
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]
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]
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。
※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。
June 14, 2025 at 1:57 PM
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。
※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。
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。
May 14, 2024 at 10:02 PM
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。
[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
(1) PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations
🔍 More at researchtrend.ai/communities/GAN
June 24, 2025 at 3:19 AM
[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
(1) PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations
🔍 More at researchtrend.ai/communities/GAN
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
September 19, 2025 at 8:10 PM
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
2. ESRGAN
The seminal Enhanced SRGAN, introducing Residual‑in‑Residual Dense Blocks (RRDB) and a relativistic discriminator to generate more natural textures.

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

April 18, 2025 at 12:40 PM
2. ESRGAN
The seminal Enhanced SRGAN, introducing Residual‑in‑Residual Dense Blocks (RRDB) and a relativistic discriminator to generate more natural textures.

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

Simon Donike, Cesar Aybar, Julio Contreras, Luis G\'omez-Chova
OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
https://arxiv.org/abs/2511.10461
OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
https://arxiv.org/abs/2511.10461
November 14, 2025 at 6:47 AM
Simon Donike, Cesar Aybar, Julio Contreras, Luis G\'omez-Chova
OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
https://arxiv.org/abs/2511.10461
OpenSR-SRGAN: A Flexible Super-Resolution Framework for Multispectral Earth Observation Data
https://arxiv.org/abs/2511.10461
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]
May 13, 2025 at 6:01 AM
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]
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]
May 13, 2025 at 6:00 AM
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]
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]
May 13, 2025 at 6:00 AM
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]
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]
May 13, 2025 at 6:00 AM
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]
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]
May 13, 2025 at 6:00 AM
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]
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
May 13, 2025 at 6:00 AM
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
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。
・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。
October 29, 2024 at 12:46 PM
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。
・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。
