Orly Liba
I'm working in the Creative Camera team, which is a part of GCam, in Machine Perception. We develop computational photography algorithms for the Pixel phones and Google Photos.
I completed my PhD at Stanford in Electrical Engineering. My research was focused on creating new computational and optical tools for seeing under the skin with OCT (optical coherence tomography).
Prior to Stanford, I worked for 4 years in the high-tech industry as an algorithms engineer on computational photography and computer vision.
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Sky Optimization: Semantically aware image processing in low-light photography
Yun-Ta Tsai
Yair Movshovitz-Attias
Huizhong Chen
(2020), pp. 526-527
Preview abstract
The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture. In nighttime photography, the sky can also suffer from noise and color artifacts. For this reason, there is a strong desire to process the sky in isolation from the rest of the scene to achieve an optimal look.
In this work, we propose an automated method, which can run as a part of a camera pipeline, for creating accurate sky alpha-masks and using them to improve the appearance of the sky.
Our method performs end-to-end sky optimization in less than half a second per image on a mobile device.
We introduce a method for creating an accurate sky-mask dataset that is based on partially annotated images that are inpainted and refined by our modified weighted guided filter. We use this dataset to train a neural network for semantic sky segmentation.
Due to the compute and power constraints of mobile devices, sky segmentation is performed at a low image resolution. Our modified weighted guided filter is used for edge-aware upsampling to resize the alpha-mask to a higher resolution.
With this detailed mask we automatically apply post-processing steps to the sky in isolation, such as automatic spatially varying white-balance, brightness adjustments, contrast enhancement, and noise reduction.
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Zoom-to-Inpaint: Image Inpainting with High Frequency Details
Huiwen Chang
Kfir Aberman
Munchurl Kim
Neal Wadhwa
Nikhil Karnad
Nori Kanazawa
Rahul Garg
Soo Ye Kim
arXiv (2020)
Preview abstract
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details. In this paper, we propose applying super resolution to coarsely reconstructed outputs, refining them at high resolution, and then downscaling the output to the original resolution. By introducing high-resolution images to the refinement network, our framework is able to reconstruct finer details that are usually smoothed out due to spectral bias - the tendency of neural networks to reconstruct low frequencies better than high frequencies. To assist training the refinement network on large upscaled holes, we propose a progressive learning technique in which the size of the missing regions increases as training progresses. Our zoom-in, refine and zoom-out strategy, combined with high-resolution supervision and progressive learning, constitutes a framework-agnostic approach for enhancing high-frequency details that can be applied to other inpainting methods. We provide qualitative and quantitative evaluations along with an ablation analysis to show the effectiveness of our approach, which outperforms state-of-the-art inpainting methods.
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Handheld Mobile Photography in Very Low Light
Kiran Murthy
Yun-Ta Tsai
Tim Brooks
Tianfan Xue
Nikhil Karnad
Qiurui He
Dillon Sharlet
Ryan Geiss
Samuel W. Hasinoff
Marc Levoy
ACM Transactions on Graphics, 38 (2019), pp. 16
Preview abstract
Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets. In this paper we describe a system for capturing clean, sharp, colorful photographs in light as low as 0.3 lux, where human vision becomes monochromatic and indistinct. To permit handheld photography without flash illumination, we capture, align, and combine multiple frames. Our system employs “motion metering”, which uses an estimate of motion magnitudes (whether due to handshake or moving objects) to identify the number of frames and the per-frame exposure times that together minimize both noise and motion blur in a captured burst. We combine these frames using robust alignment and merging techniques that are specialized for high-noise imagery. To ensure accurate colors in such low light, we employ a learning-based auto white balancing algorithm. To prevent the photographs from looking like they were shot in daylight, we use tone mapping techniques inspired by illusionistic painting: increasing contrast, crushing shadows to black, and surrounding the scene with darkness. All of these processes are performed using the limited computational resources of a mobile device. Our system can be used by novice photographers to produce shareable pictures in a few seconds based on a single shutter press, even in environments so dim that humans cannot see clearly.
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