Depth Map Estimation Using Defocus and Motion Cues

Kumar, Himanshu; Yadav, Ajeet Singh; Gupta, Sumana; Venkatesh, K. S.

doi:10.1109/tcsvt.2018.2832086

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…Nazir et al [20] suggested a deep convolutional neural network to estimate the depth and image deblurring. Kumar et al [21] presented a novel technique to generate a more accurate depth map for dynamic scenes using a combination of defocus and motion cues. The combination was performed by keeping the parameter of the defocus edge points aligned in the motion direction and estimating the camera parameters with the help of motion and defocus relations.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Proof of the State-of-the-Art Geometric Hypothesis on Depth-of-Field Scaling and Shifting Influence on Image Sharpness

Khatibi,

Wen,

Emam

2024

Applied Sciences

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Today, we capture and store images in a way that has never been possible. However, huge numbers of degraded and blurred images are captured unintentionally or by mistake. In this paper, we propose a geometrical hypothesis stating that blurring occurs by shifting or scaling the depth of field (DOF). The validity of the hypothesis is proved by an independent method based on depth estimation from a single image. The image depth is modeled regarding its edges to extract amplitude comparison ratios between the generated blurred images and the sharp/blurred images. Blurred images are generated by a stepwise variation in the standard deviation of the Gaussian filter estimate in the improved model. This process acts as virtual image recording used to mimic the recording of several image instances. A historical documentation database is used to validate the hypothesis and classify sharp images from blurred ones and different blur types. The experimental results show that distinguishing unintentionally blurred images from non-blurred ones by a comparison of their depth of field is applicable.

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Section: Related Workmentioning

confidence: 99%

Learning-Based Proof of the State-of-the-Art Geometric Hypothesis on Depth-of-Field Scaling and Shifting Influence on Image Sharpness

Khatibi,

Wen,

Emam

2024

Applied Sciences

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“…[30]- [39] adopted different strategies to obtain reliable depth estimation from a single camera by learning to exploit monocular clues such as shadows, occlusions and relative scales between objects. In this field, a particularly appealing practice consists of training end-to-end models in a self or semisupervised manner [12], [36], replacing the need for groundtruth depth labels with image reprojection across different viewpoints according to two main strategies, respectively acquiring images with a single, moving camera [36], [38], [40], [41] or using a stereo camera [10], [12], [37], [42]- [44].…”

Section: A Depth Estimationmentioning

confidence: 99%

Monocular Depth Perception on Microcontrollers for Edge Applications

Peluso

Cipolletta

Calimera

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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Depth estimation is crucial in several computer vision applications, and a recent trend in this field aims at inferring such a cue from a single camera. Unfortunately, despite the compelling results achieved, state-of-the-art monocular depth estimation methods are computationally demanding, thus precluding their practical deployment in several application contexts characterized by low-power constraints. Therefore, in this paper, we propose a lightweight Convolutional Neural Network based on a shallow pyramidal architecture, referred to as µPyD-Net, enabling monocular depth estimation on microcontrollers. The network is trained in a peculiar self-supervised manner leveraging proxy labels obtained through a traditional stereo algorithm. Moreover, we propose optimization strategies aimed at performing computations with quantized 8-bit data and map the high-level description of the network to low-level layers optimized for the target microcontroller architecture. Exhaustive experimental results on standard datasets and an in-depth evaluation with a device belonging to the popular Arm Cortex-M family confirm that obtaining sufficiently accurate monocular depth estimation on microcontrollers is feasible. To the best of our knowledge, our proposal is the first one enabling such remarkable achievement, paving the way for the deployment of monocular depth cues onto the tiny end-nodes of distributed sensor networks.

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“…With the development of computer vision, depth estimation [14,30,34], defocus estimation [2,32,7] and saliency detection [54,38,43,56] have made significant progress, which also provide more directions for solving bokeh rendering tasks. Many methods [48,37,11,17] utilize these prior knowledge to synthesize bokeh effects.…”

Section: Related Workmentioning

confidence: 99%

Constrained Predictive Filters for Single Image Bokeh Rendering

Zheng

Chen

Yuan

et al. 2022

IEEE Trans. Comput. Imaging

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Bokeh rendering is a technique used to take pictures with out-of-focus areas to highlight regions of interest. Due to limitations in hardware and shooting condition, rendering a bokeh image from a full-focus image has attracted a lot of interest. In this paper, we model bokeh rendering as the combination of salient region retention and bokeh blurring, and propose a neural network to generate a realistic bokeh image from a single full-focus image through end-to-end training. Specifically, we propose a gate fusion block to estimate the salient area, and introduce a constrained predictive filter for salient region retention and bokeh blurring within a unified architecture. Further, we utilize a pixel coordinate-based map to enhance the training. Experimental results illustrate the effectiveness of our model. The comparison with state-of-the-art methods (PyNET [18], DMSHN [12], BGGAN [40], etc.) shows that our model produces better bokeh effects and retains salient objects.

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Depth Map Estimation Using Defocus and Motion Cues

Cited by 17 publications

References 31 publications

Learning-Based Proof of the State-of-the-Art Geometric Hypothesis on Depth-of-Field Scaling and Shifting Influence on Image Sharpness

Learning-Based Proof of the State-of-the-Art Geometric Hypothesis on Depth-of-Field Scaling and Shifting Influence on Image Sharpness

Monocular Depth Perception on Microcontrollers for Edge Applications

Constrained Predictive Filters for Single Image Bokeh Rendering

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