Results 81 to 90 of about 15,547 (189)
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that ...
Chen, Huaijin +5 more
core +1 more source
Multi‐Scale Transformer for Image Restoration
ABSTRACT Although Transformer‐based image restoration methods have demonstrated impressive performance, existing Transformers still insufficiently exploit multiscale information. Previous non‐Transformer‐based studies have shown that incorporating multiscale features is crucial for improving restoration results.
Wuzhen Shi +6 more
wiley +1 more source
Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases
Single‐cell RNA sequencing and spatial transcriptomics have unveiled cellular heterogeneity and tissue organization with unprecedented resolution. Artificial intelligence (AI) now plays a pivotal role in interpreting these complex data. This review systematically surveys AI applications across the entire analytic workflow and offers practical guidance ...
Shixin Li +7 more
wiley +1 more source
Abstract Purpose To analytically define a spiral waveform and trajectory that match the constraints of gradient frequency, slew rate, and amplitude. Theory and Methods Piecewise analytical solutions for gradient waveforms under the desired constraints are derived using the circle of an involute rather than an Archimedean spiral.
Guruprasad Krishnamoorthy, James G. Pipe
wiley +1 more source
BM3D Frames and Variational Image Deblurring
A family of the Block Matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patch-wise image modeling [1], [2].
Danielyan, Aram +2 more
core +1 more source
Enhancing convolutional neural network generalizability via low‐rank weight approximation
A self‐supervised framework is proposed for image denoising based on the Tucker low‐rank tensor approximation. With the proposed design, we are able to characterize our denoiser with fewer parameters and train it based on a single image, which considerably improves the model's generalizability and reduces the cost of data acquisition. Abstract Noise is
Chenyin Gao, Shu Yang, Anru R. Zhang
wiley +1 more source
Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should
Banerjee, Sreya +4 more
core +1 more source
This paper proposes a low‐light image enhancement and denoising algorithm tailored for tunnel scenes based on computer vision and deep learning technologies. On this basis, a tunnel pedestrian detection method based on connected domain dynamic threshold segmentation is designed, which can reduce the computational resources for identifying pedestrian ...
Yudan Tian +4 more
wiley +1 more source
Simulated Artifacts and Data Augmentation for Real-World Video Motion Deblurring
This paper proposes a data augmentation method that simulates artifacts specific to real-world videos as a preprocessing step for applying a deep learning-based video deblurring method to real-world videos.
Sota Moriyama, Kaito Kira, Koichi Ichige
doaj +1 more source
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulated over time during the exposure of the sensor.
Favaro, Paolo +2 more
core +1 more source

