Enhanced Deep Residual Networks for Single Image Super-Resolution [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance.
Kim, Heewon +4 more
core +2 more sources
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network [PDF]
IEEE Access, 2020 In many domestic and military applications, aerial vehicle detection and super-resolution algorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack
Moktari Mostofa +3 more
doaj +4 more sources
Image Super-Resolution via Iterative Refinement [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al.
Chitwan Saharia +5 more
semanticscholar +1 more source
Image Super Resolution using Enhanced Super Resolution Generative Adversarial Network [PDF]
ITM Web of Conferences, 2022 Aside from enhancing the accuracy and speed of single picture modification utilizing fast and in-depth convolutional emotional networks, one significant challenge remains mostly commonly unaddressed, namely how do we recover soft texture details when we ...
Sarode Raj +3 more
doaj +1 more source
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Xintao Wang +3 more
semanticscholar +1 more source
Activating More Pixels in Image Super-Resolution Transformer [PDF]
Computer Vision and Pattern Recognition, 2022 Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis.
Xiangyu Chen +3 more
semanticscholar +1 more source
Terahertz time-domain attenuated total reflection spectroscopy integrated with a microfluidic chip
Frontiers in Bioengineering and Biotechnology, 2023 The integration of a microfluidic chip into terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy is highly demanded for the accurate measurement of aqueous samples.
Ying Fu +14 more
doaj +1 more source
Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super ...
YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin
doaj +1 more source
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution [PDF]
Computer Vision and Pattern Recognition, 2023 Owe to the powerful generative priors, the pretrained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem.
Rongyuan Wu +5 more
semanticscholar +1 more source
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting [PDF]
Neural Information Processing Systems, 2023 Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps.
Zongsheng Yue +2 more
semanticscholar +1 more source