Results 21 to 30 of about 2,065,913 (297)
Blind Image Quality Assessment for Super Resolution via Optimal Feature Selection
IEEE Access, 2020 Methods for image Super Resolution (SR) have started to benefit from the development of perceptual quality predictors that are designed for super resolved images.
Juan Beron +2 more
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Opto-Electronic Advances, 2023 Structured illumination microscopy (SIM) is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity.
Elizabeth Abraham +2 more
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Blind Super-Resolution With Iterative Kernel Correction [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic).
Jinjin Gu +3 more
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Super-resolution of remotely sensed images with variable-pixel linear reconstruction [PDF]
, 2007 This paper describes the development and applications of a super-resolution method, known as Super-Resolution Variable-Pixel Linear Reconstruction. The algorithm works combining different lower resolution images in order to obtain, as a result, a higher ...
Núñez de Murga, Jorge, 1955- +1 more
core +1 more source
BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK
Iraqi Journal of Information & Communication Technology, 2021 Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image.
Meryem H. Muhson, Ayad A. Al-Ani
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Blind Super-Resolution via Projected Gradient Descent
2022 IEEE International Symposium on Information Theory (ISIT), 2022 Blind super-resolution can be cast as low rank matrix recovery problem by exploiting the inherent simplicity of the signal. In this paper, we develop a simple yet efficient nonconvex method for this problem based on the low rank structure of the vectorized Hankel matrix associated with the target matrix.
Sihan Mao, Jinchi Chen
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Sensors, 2022 To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution–high-resolution (LR–HR) pairs or (B) predict the degradations of an LR image and then use these to
Matthew Aquilina +5 more
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Unfolding the Alternating Optimization for Blind Super Resolution
CoRR, 2020 Conference on Neural Information Processing Systems (NeurIPS ...
Zhengxiong Luo 0001 +4 more
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Contrastive learning for a single historical painting’s blind super-resolution
Visual Informatics, 2021 Most of the existing blind super-resolution(SR) methods explicitly estimate the kernel in pixel space, which usually has a large deviation and results in poor SR performance.
Hongzhen Shi +4 more
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Unsupervised Degradation Representation Learning for Blind Super-Resolution [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these methods suffer a severe performance drop when the real degradation is different from their assumption. To handle various unknown degradations in real-world applications, previous
Longguang Wang +6 more
openaire +3 more sources