SupRes: Facial Image Upscaling Using Sparse Denoising Autoencoder

Even in this era of digital images, still many images and media are hazy, pixelated, and blurry. This could be due to low-quality imaging sensors, poor image stabilization, or the image itself being old. This study proposes the Sparse Denoising Autoencoders (SDAEs) for upscaling blurry images. The p...

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Veröffentlicht in:2023 7th International Conference on Computing Methodologies and Communication (ICCMC) S. 541 - 546
Hauptverfasser: Agrawal, Manan, Anwar, Mohd Ayaan, Saroha, Nakul, Goel, Anurag
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 23.02.2023
Schlagworte:
Convolutional Neural Networks
Deep architecture
Digital images
Focusing
Generative Adversarial Networks
Image quality
Image sensors
Image Upscaling
Neural networks
Noise reduction
Sparse Denoising Autoencoders
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Zusammenfassung:Even in this era of digital images, still many images and media are hazy, pixelated, and blurry. This could be due to low-quality imaging sensors, poor image stabilization, or the image itself being old. This study proposes the Sparse Denoising Autoencoders (SDAEs) for upscaling blurry images. The performance of the proposed SDAEs is then compared with the deep learning architecture, Pix2Pix Generative Adversarial Networks (GANs) by primarily focusing on the facial images. The experimental results show that the SDAEs give slightly better results than GANs. Additionally, the SDAE architecture is computationally 30% efficient when compared to the Pix2Pix GAN.
DOI:10.1109/ICCMC56507.2023.10083628