Results 31 to 40 of about 216,992 (320)
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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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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Kernel-aware Burst Blind Super-Resolution
, 2021 Burst super-resolution (SR) technique provides a possibility of restoring rich details from low-quality images. However, since real world low-resolution (LR) images in practical applications have multiple complicated and unknown degradations, existing non-blind (e.g., bicubic) designed networks usually suffer severe performance drop in recovering high ...
Lian, Wenyi, Peng, Shanglian
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Blind Image Super-Resolution: A Survey and Beyond
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Blind image super-resolution (SR), aiming to super-resolve low-resolution images with unknown degradation, has attracted increasing attention due to its significance in promoting real-world applications. Many novel and effective solutions have been proposed recently, especially with the powerful deep learning techniques.
Anran Liu +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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A super- resolution feasibility study in small-animal SPECT imaging [PDF]
, 2008 Proceeding of: 2008 IEEE Nuclear Science Symposium Conference Record (NSS '08), Dresden, Germany, 19-25 Oct. 2008Lack of spatial resolution is a major drawback in small-animal SPECT imaging, particularly when parallel hole collimators are employed.
Carlos, Álvaro de +6 more
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Deep Blind Video Super-resolution
, 2020 Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for video SR and usually leads to over-smoothed super-resolved images.
Pan, Jinshan +3 more
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Blind assessment of localisation microscope image resolution [PDF]
, 2012 Background This paper analyses the resolution achieved in localisation microscopy experiments. The resolution is an essential metric for the correct interpretation of super-resolution images, but it varies between specimens due to different localisation ...
Erdelyi, M +5 more
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Meta-Learning-Based Degradation Representation for Blind Super-Resolution
IEEE Transactions on Image Processing, 2023 The most of CNN based super-resolution (SR) methods assume that the degradation is known (\eg, bicubic). These methods will suffer a severe performance drop when the degradation is different from their assumption. Therefore, some approaches attempt to train SR networks with the complex combination of multiple degradations to cover the real degradation ...
Bin Xia +5 more
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End-to-End Alternating Optimization for Real-World Blind Super Resolution [PDF]
International Journal of Computer Vision, 2023 Blind super-resolution (SR) usually involves two sub-problems: (1) estimating the degradation of the given low-resolution (LR) image; (2) super-resolving the LR image to its high-resolution (HR) counterpart.
Zhengxiong Luo +4 more
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