Unleashing the Power of SRGAN and ESRGAN: Revolutionizing Image Super-Resolution | Practical Demo

GAN Zoo: github.com/hin...
SRGAN: github.com/ent...
ESRGAN: github.com/xin...
Welcome to my latest video, where we dive deep into the world of image super-resolution and explore two groundbreaking algorithms: SRGAN and ESRGAN. Get ready to witness a leap forward in image enhancement that will leave you in awe!
Super-Resolution Generative Adversarial Networks (SRGAN) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) have emerged as game-changers in the field of computer vision. These cutting-edge algorithms utilize the power of deep learning and artificial intelligence to transform low-resolution images into incredibly detailed and lifelike high-resolution masterpieces.
In this video, we take you on a journey through the fundamentals of SRGAN and ESRGAN, explaining the underlying concepts and showcasing their astonishing capabilities. You'll discover how these algorithms leverage the power of Generative Adversarial Networks (GANs) and deep convolutional neural networks to generate visually stunning images that were previously unimaginable.
Join us as we discuss the key features and advantages of SRGAN and ESRGAN, including their ability to preserve fine details, enhance textures, and produce more realistic and natural-looking images. We'll also explore how these algorithms address common challenges such as image artifacts and noise, ensuring superior quality in the final output.
But that's not all! We'll demonstrate the practical applications of SRGAN and ESRGAN across various industries, including digital art, photography, medical imaging, and more. Witness how these algorithms revolutionize image restoration, upscaling, and enhancement, opening up new possibilities for professionals and enthusiasts alike.
Whether you're a tech enthusiast, a creative professional, or simply curious about the latest advancements in computer vision, this video is a must-watch. Prepare to be amazed as we unveil the potential of SRGAN and ESRGAN and the impact they are making on the future of image super-resolution.
Don't miss out on this captivating exploration of SRGAN and ESRGAN. Hit that play button, and let's embark on this extraordinary journey together!
#SRGAN #ESRGAN #ImageSuperResolution #DeepLearning #ComputerVision #ArtificialIntelligence
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This channel focuses on providing content on Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural language processing, Python programming, etc. in Bangla and English.
My mission is to provide inspiration, motivation & good quality education to students for learning and human development, and to become an expert in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Natural language processing, Python programming, and so on.
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[@jannataziz2436]

Your way of explaining and implementing is very effective and easy. Thank you bappy! I did search alot for this video but no body hits. Great job!

[@bunnyfans7234]

Thanks bhai

[@uiuxshoaib]

Thanks man, this is the only method that worked for me :)

[@Akashphs7217]

Thanks a lot bro. Also i noticed that this model works for face (human, animal etc.). I tried it on a blurred image of rose, it didn't work well. Any suggestion?

[@dswithbappy]

try with different variant of model

[@shuvroislam3836]

❤

[@RolanRoyce]

It only works with the sample images they provide, my own images came out exactly the same as they went in the LR folder, no noticeable difference whatsoever.

[@Dinesh-wc8jw]

me also facing the same issue bro

[@RolanRoyce]

One called REALESRGAN Portable worked though.@@Dinesh-wc8jw

[@tondamnarendra9277]

Can you please make videos on ML

[@RahulPrajapati-jg4dg]

Can you create end to end implementation using data ingestion to data transformation to training and deployment

[@sunilkumarpradhan.4376]

the way you explain is just beautiful !! Thanks a Lot bappy !!

[@ashirvaadbhat4234]

Error: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU. As I don't have the GPU so what changes I have to make ??

[@SaiNandiniRanga]

Use google colab

[@sahilpatildreamer]

Runtime-use GPU

[@vigneshvicky6720]

Tq❤

[@qln1493]

Ok

[@josephpeters7076]

I was cracking up the entire time

[@tondamnarendra9277]

❤