Perceptual Image Enhancement for Smartphone Real-Time Applications

Conde, Marcos V.; Vasluianu, Florin; Vázquez-Corral, Javier; Timofte, Radu

doi:10.1109/wacv56688.2023.00189

Cited by 22 publications

(1 citation statement)

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“…In the recent years blind image restoration and superresolution using deep learning has become a popular task [13,16,18,34,51,64,69,74], this is because blind methods do not require prior knowledge about the degradation factors or the camera sensor, therefore they are general purpose models i.e. practically, such methods can process any degraded RGB image.…”

Section: Image Super-resolution and Restorationmentioning

confidence: 99%

Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration

Conde

Choi²,

Burchi

et al. 2023

Lecture Notes in Computer Science

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In smartphones and compact cameras, the Image Signal Processor (ISP) transforms the RAW sensor image into a human-readable sRGB image. Most popular superresolution methods depart from a sRGB image and upscale it further, improving its quality. However, modeling the degradations in the sRGB domain is complicated because of the non-linear ISP transformations. Despite this known issue, only a few methods work directly with RAW images and tackle real-world sensor degradations.We tackle blind image super-resolution in the RAW domain. We design a realistic degradation pipeline tailored specifically for training models with raw sensor data. Our approach considers sensor noise, defocus, exposure, and other common issues. Our BSRAW models trained with our pipeline can upscale real-scene RAW images and improve their quality. As part of this effort, we also present a new DSLM dataset and benchmark for this task.

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