Auto Removal of Bright Spot from Images Captured Against Flashing Light Source

Asha, C S; Bhat, Sooraj; Nayak, Deepa; Bhat, C. L.

doi:10.1109/discover47552.2019.9007933

Cited by 18 publications

(3 citation statements)

References 9 publications

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“…Notably, lens flare or glare is another common lens artifact that plagues the photography in which the scene is also partially obstructed. Nonetheless, existing solutions [4,33,43] focus on single image restoration which is inherently illposed, whereas our method explicitly utilizes multi-frame information captured by moving cameras.…”

Section: Related Workmentioning

confidence: 99%

Let's See Clearly: Contaminant Artifact Removal for Moving Cameras

Li¹,

Zhang²,

Liao³

et al. 2021

Preprint

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show abstract

Section: Related Workmentioning

confidence: 99%

Let's See Clearly: Contaminant Artifact Removal for Moving Cameras

Li¹,

Zhang²,

Liao³

et al. 2021

Preprint

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“…texture separation [25], underwater image restoration [20,28], image desnowing [50,61], stereo mixture image decomposition [81], 3D intrinsic image decomposition [1], fence removal [12,71,51], flare removal [70,3] are covered inside image decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…This paper aims at addressing the aforementioned gap, as an important step toward real-world vision systems. We propose a task that: (1) does not fix the number of source components, (2) considers the presence of every source component and varying intensities of source components, and (3) amalgamates every source component as potential combinations. To disambiguate with previous tasks, we refer to our proposed task as Blind Image Decomposition (BID).…”

Section: Introductionmentioning

confidence: 99%

Blind Image Decomposition

Han¹,

Li²,

Fang³

et al. 2021

Preprint

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We present and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step towards real-world vision systems. To facilitate research on this new task, we construct three benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN. Code and project page are available.

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Blind Image Decomposition

Han

Fang

et al. 2022

Lecture Notes in Computer Science

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Auto Removal of Bright Spot from Images Captured Against Flashing Light Source

Cited by 18 publications

References 9 publications

Let's See Clearly: Contaminant Artifact Removal for Moving Cameras

Let's See Clearly: Contaminant Artifact Removal for Moving Cameras

Blind Image Decomposition

Blind Image Decomposition

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