11 research outputs found
RELLISUR: A Real Low-Light Image Super-Resolution Dataset
The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution
Real-world super-resolution of face-images from surveillance cameras
Most existing face image Super-Resolution (SR) methods assume that the
Low-Resolution (LR) images were artificially downsampled from High-Resolution
(HR) images with bicubic interpolation. This operation changes the natural
image characteristics and reduces noise. Hence, SR methods trained on such data
most often fail to produce good results when applied to real LR images. To
solve this problem, we propose a novel framework for generation of realistic
LR/HR training pairs. Our framework estimates realistic blur kernels, noise
distributions, and JPEG compression artifacts to generate LR images with
similar image characteristics as the ones in the source domain. This allows us
to train a SR model using high quality face images as Ground-Truth (GT). For
better perceptual quality we use a Generative Adversarial Network (GAN) based
SR model where we have exchanged the commonly used VGG-loss [24] with
LPIPS-loss [52]. Experimental results on both real and artificially corrupted
face images show that our method results in more detailed reconstructions with
less noise compared to existing State-of-the-Art (SoTA) methods. In addition,
we show that the traditional non-reference Image Quality Assessment (IQA)
methods fail to capture this improvement and demonstrate that the more recent
NIMA metric [16] correlates better with human perception via Mean Opinion Rank
(MOR)