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Author

Shaojie Zhuo

Bio: Shaojie Zhuo is an academic researcher from Qualcomm. The author has contributed to research in topic(s): Image restoration & Iterative reconstruction. The author has an hindex of 18, co-authored 23 publication(s) receiving 1462 citation(s). Previous affiliations of Shaojie Zhuo include National University of Singapore.
Papers
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Journal ArticleDOI
Shaojie Zhuo1, Terence Sim1Institutions (1)
TL;DR: This paper presents a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations, and demonstrates the effectiveness of this method in providing a reliable estimation of the defocus map.
Abstract: In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. By propagating the blur amount at edge locations to the entire image, a full defocus map can be obtained. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimation of the defocus map.

313 citations


Proceedings ArticleDOI
Alex Yong-Sang Chia1, Shaojie Zhuo2, Raj Kumar Gupta3, Yu-Wing Tai4  +3 moreInstitutions (5)
12 Dec 2011
TL;DR: A colorization system that leverages the rich image content on the internet and the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene to achieve the desired result.
Abstract: Colorization of a grayscale photograph often requires considerable effort from the user, either by placing numerous color scribbles over the image to initialize a color propagation algorithm, or by looking for a suitable reference image from which color information can be transferred. Even with this user supplied data, colorized images may appear unnatural as a result of limited user skill or inaccurate transfer of colors. To address these problems, we propose a colorization system that leverages the rich image content on the internet. As input, the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene. With this information, images are downloaded from photo sharing websites and filtered to obtain suitable reference images that are reliable for color transfer to the given grayscale photo. Different image colorizations are generated from the various reference images, and a graphical user interface is provided to easily select the desired result. Our experiments and user study demonstrate the greater effectiveness of this system in comparison to previous techniques.

215 citations


Patent
Chen Feng1, Xiaopeng Zhang1, Shaojie Zhuo1, Liang Shen1  +2 moreInstitutions (1)
15 Jul 2014
Abstract: Certain aspects relate to systems and techniques for generating high resolution iris templates and for detecting spoofs, enabling more reliable and secure iris authentication. Pairs of RGB and NIR images can be captured by the iris authentication system for use in iris authentication, for example using an NIR LED flash and a four-channel image sensor. Multiple images of the user's iris can be captured by the system in a relatively short period of time and can be fused together to generate a high resolution iris image that can contain more detail of the iris structure and unique pattern than each individual images. The “liveness” of the iris, referring to whether the iris is a real human iris or an iris imitation, can be assessed via a liveness ratio based on comparison of known iris and sclera reflectance properties at various wavelengths to determined sensor responses at those same wavelengths.

90 citations


Proceedings ArticleDOI
Qiong Yan1, Xiaoyong Shen1, Li Xu1, Shaojie Zhuo2  +3 moreInstitutions (4)
01 Dec 2013
TL;DR: A scale map is introduced as a competent representation to explicitly model derivative-level confidence and new functions and a numerical solver are proposed to effectively infer it following new structural observations.
Abstract: Color, infrared, and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images in different fields, for example, one noisy color image and one dark-flashed near infrared image. The major issue in such a framework is to handle structure divergence and find commonly usable edges and smooth transition for visually compelling image reconstruction. We introduce a scale map as a competent representation to explicitly model derivative-level confidence and propose new functions and a numerical solver to effectively infer it following new structural observations. Our method is general and shows a principled way for cross-field restoration.

86 citations


Proceedings ArticleDOI
Chen Feng1, Shaojie Zhuo1, Xiaopeng Zhang1, Liang Shen1  +1 moreInstitutions (2)
01 Sep 2013
TL;DR: An improved image dehazing scheme using a pair of color and NIR images, which effectively estimates the airlight color and transfers details from the NIR, and can achieve substantial improvements on the detail recovery and the color distribution over the existing imageDehazing algorithms.
Abstract: Near-infrared (NIR) light has stronger penetration capability than visible light due to its long wavelengths and is thus less scattered by particles in the air This makes it desirable for image dehazing to unveil details of distant objects in landscape photographs In this paper, we propose an improved image dehazing scheme using a pair of color and NIR images, which effectively estimates the airlight color and transfers details from the NIR A two-stage dehazing method is proposed by exploiting the dissimilarity between RGB and NIR for airlight color estimation, followed by a dehazing procedure through an optimization framework Experiments on captured haze images show that our method can achieve substantial improvements on the detail recovery and the color distribution over the existing image dehazing algorithms

86 citations


Cited by
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Proceedings ArticleDOI
Ming-Ming Cheng1, Guo-Xin Zhang1, Niloy J. Mitra2, Xiaolei Huang3  +1 moreInstitutions (3)
20 Jun 2011
TL;DR: This work proposes a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence, and consistently outperformed existing saliency detection methods.
Abstract: Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

3,388 citations


Book ChapterDOI
08 Oct 2016
TL;DR: This paper proposes a fully automatic approach to colorization that produces vibrant and realistic colorizations and shows that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder.
Abstract: Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a “colorization Turing test,” asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32 % of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.

1,949 citations


Journal ArticleDOI
Ali Borji1, Ming-Ming Cheng2, Huaizu Jiang3, Jia Li4Institutions (4)
TL;DR: It is found that the models designed specifically for salient object detection generally work better than models in closely related areas, which provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems.
Abstract: We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.

1,372 citations


Posted Content
Abstract: Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a "colorization Turing test," asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32% of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.

1,284 citations


Proceedings ArticleDOI
23 Jun 2014
TL;DR: It is observed that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size, so as to train a generic objectness measure.
Abstract: Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.

1,034 citations


Network Information
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Performance
Metrics

Author's H-index: 18

No. of papers from the Author in previous years
YearPapers
20193
20182
20171
20153
20143
20133