235 research outputs found
Highly sensitive transient absorption imaging of graphene and graphene oxide in living cells and circulating blood
We report a transient absorption (TA) imaging method for fast visualization and quantitative layer analysis of graphene and GO. Forward and backward imaging of graphene on various substrates under ambient condition was imaged with a speed of 2 μs per pixel. The TA intensity linearly increased with the layer number of graphene. Real-time TA imaging of GO in vitro with capability of quantitative analysis of intracellular concentration and ex vivo in circulating blood were demonstrated. These results suggest that TA microscopy is a valid tool for the study of graphene based materials
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the
human perception of image quality without any reference information. We develop
a general and automated multitask learning scheme for BIQA to exploit auxiliary
knowledge from other tasks, in a way that the model parameter sharing and the
loss weighting are determined automatically. Specifically, we first describe
all candidate label combinations (from multiple tasks) using a textual
template, and compute the joint probability from the cosine similarities of the
visual-textual embeddings. Predictions of each task can be inferred from the
joint distribution, and optimized by carefully designed loss functions. Through
comprehensive experiments on learning three tasks - BIQA, scene classification,
and distortion type identification, we verify that the proposed BIQA method 1)
benefits from the scene classification and distortion type identification tasks
and outperforms the state-of-the-art on multiple IQA datasets, 2) is more
robust in the group maximum differentiation competition, and 3) realigns the
quality annotations from different IQA datasets more effectively. The source
code is available at https://github.com/zwx8981/LIQE.Comment: CVPR202
- …