Qiyuan Tian

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A major challenge in understanding the cellular diversity of the brain has been linking activity during behavior with standard cellular typology. For example, it has not been possible to determine whether principal neurons in prefrontal cortex active during distinct experiences represent separable cell types, and it is not known whether these differentially(More)
The high density of pixels in modern color sensors provides an opportunity to experiment with new color filter array (CFA) designs. A significant bottleneck in evaluating new designs is the need to create demosaicking, denoising and color transform algorithms tuned for the CFA. To address this issue, we developed a method(local, linear, learned or L 3) for(More)
To speed the development of novel camera architectures we proposed a method, L 3 (Local, Linear and Learned), that automatically creates an optimized image processing pipeline. The L 3 method assigns each sensor pixel into one of 400 classes, and applies class-dependent local linear transforms that map the sensor data from a pixel and its neighbors into the(More)
This paper presents a very fast local tone mapping method for displaying high dynamic range (HDR) images. Though local tone mapping operators produce better local contrast and details, they are usually slow. We have solved this problem by designing a highly parallel algorithm, which can be easily implemented on a Graphics Processing Unit (GPU) to harvest(More)
This paper presents a novel contrast enhancement algorithm based on local histogram modification. A global method, which works by striking a balance between linear contrast enhancement and histogram equalization, is firstly introduced. We then segment images into rectangle regions and adaptively stretch contrast using our global enhancement algorithmin in(More)
The development of an image processing pipeline for each new camera design can be time-consuming. To speed camera development, we developed a method named L 3 (Local, Linear, Learned) that automatically creates an image processing pipeline for any design. In this paper, we describe how we used the L 3 method to design and implement an image processing(More)
PURPOSE To characterize the q-space truncation and sampling on the spin-displacement probability density function (PDF) in diffusion spectrum imaging (DSI). METHODS DSI data were acquired using the MGH-USC connectome scanner (Gmax  = 300 mT/m) with bmax  = 30,000 s/mm(2) , 17 × 17 × 17, 15 × 15 × 15 and 11 × 11 × 11 grids in ex vivo human brains and bmax(More)