Results 31 to 40 of about 42,664 (162)
A Progressive Decoupled Network for Blind Image Super-Resolution
Blind super-resolution (Blind SR) has become a popular research topic in computer vision in super-resolution, which aims to enhance low-resolution (LR) images with unknown or partially known degradation blur kernels.
Laigan Luo, Benshun Yi, Chao Zhu
doaj +1 more source
The Perception-Distortion Tradeoff
Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality.
Blau, Yochai, Michaeli, Tomer
core +1 more source
"Zero-Shot" Super-Resolution using Deep Internal Learning
Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their ...
Cohen, Nadav +2 more
core +1 more source
A semiconductor‐fabricated nanowell biosensor enables rapid, scalable, and highly reproducible detection of SARS‐CoV‐2 antigens from nasal swabs within ∼10 minutes. Clinical validation in 249 retrospective and 243 prospective patient samples demonstrates high sensitivity and specificity, minimal cross‐reactivity, and robust batch‐to‐batch ...
Yoo Min Park +11 more
wiley +1 more source
Mid‐infrared photothermal imaging enables multidimensional profiling of micro‐ and nanoplastics in bottled water. A total of 9.9 × 104 particles L−1 is detected, with 64% in the nanoscale regime. Spectral evolution, including peak narrowing and band shifts, reveals local chain reorganization in polyethylene terephthalate (PET), highlighting intrinsic ...
Xinyu Deng +4 more
wiley +1 more source
CN-BSRIQA: Cascaded network - blind super-resolution image quality assessment
High resolution (HR) images consist of higher quality and more detail information in comparison to low-resolution images. But obtaining HR images entails higher costs and requires a larger workforce.
Mobeen Ur Rehman +5 more
doaj +1 more source
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images.
Chen, Yu +4 more
core +1 more source
This study reported renal‐clearable bio‐orthogonal near‐infrared fluorogenic probes (BGRs) that specifically imaging and urinalysis of granzyme B for dynamic evaluation of RCC immunotherapy. BGRs not only differentiate immunotherapeutic responses in orthotopic RCC mice, but also enable sensitive optical urinalysis of granzyme B in clinical specimens ...
Xingyue Yang +9 more
wiley +1 more source
In current super-resolution (SR) research, blind SR models capable of handling multiple degradations have attracted significant attention. Inspired by variational autoencoders (VAEs) that model data distributions through latent representations, this ...
Ning Zhang +4 more
doaj +1 more source
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self ...
Rothe, Rasmus +2 more
core +1 more source

