In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8× and 2.9× speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3× speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4× speedup. With HINet, we won the 1st place on the NTIRE 2021 Image Deblurring Challenge -Track2. JPEG Artifacts, with a PSNR of 29.70. The code is available at https://github.com/megviimodel/HINet.
Creativity is the ability to see the world in new ways. Creative individuals exhibit the ability to switch between different modes of thinking and shift their mental focus. This suggests a connection between creativity and dynamic interactions of brain networks. We report here the first investigation into the relationship between the reconfiguration of dynamic brain networks during the resting state and verbal creativity using two fMRI datasets involving 574 subjects. We find that verbal creativity correlates with temporal variability of the functional-connectivity (FC) patterns of the lateral prefrontal cortex, the precuneus, and the parahippocampal gyrus. High variability of these regions indicates flexible connectivity patterns which may facilitate executive functions. Furthermore, verbal creativity correlates with the temporal variability of FC patterns within the default mode network (DMN), between the DMN and attention/sensorimotor network, and between control and sensory networks. High variability of FCs between the DMN and attention networks characterizes frequent adjustments of attention. Finally, dynamic interaction between the cerebellum and task control network also contributes to verbal creativity, suggesting a relationship between the cerebellum and creativity. This study reveals a close relationship between verbal creativity and high variability of cortical networks involved in spontaneous thought, attention and cognitive control.
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