Fast Detection and Removal of Glare in Gray Scale Laparoscopic Images

Lamprinou, Nefeli; Psarakis, Emmanouil Ζ.

doi:10.5220/0006654202060212

Cited by 3 publications

(2 citation statements)

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“…The presence of a glare in an image typically reduces its legibility and aesthetics: glare can cause loss of information in underlying pixels, make the image harder to interpret, reduce contrast and color variety. Practical applications of glare removal are numerous: uterine cervix cancer detection [1] and enhancement of laparoscopic images [2], vehicle license plate recognition [3], eyeglass reflection glare from frontal face shots [4] -both for aesthetic improvement of photos and as a part of recognition pipeline -and others. Typically, glare is caused by bright sources of light in the scene, however it can also be caused by water droplets on the camera lens [5].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the used datasets consider only dozens of images and are too small to use as a training dataset. Three large datasets correspond to very narrow cases: [2] provides images of laparoscopic images distorted by specular reflection; [4] considers the face images with eyeglass reflection glare and [3] uses a dataset of road signs with added synthetic glares.…”

Section: Introductionmentioning

confidence: 99%

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Veiling glare removal: synthetic dataset generation, metrics and neural network architecture

Shoshin

Shvets

2021

Computer Optics

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In photography, the presence of a bright light source often reduces the quality and readability of the resulting image. Light rays reflect and bounce off camera elements, sensor or diaphragm causing unwanted artifacts. These artifacts are generally known as "lens flare" and may have different influences on the photo: reduce contrast of the image (veiling glare), add circular or circular-like effects (ghosting flare), appear as bright rays spreading from light source (starburst pattern), or cause aberrations. All these effects are generally undesirable, as they reduce legibility and aesthetics of the image. In this paper we address the problem of removing or reducing the effect of veiling glare on the image. There are no available large-scale datasets for this problem and no established metrics, so we start by (i) proposing a simple and fast algorithm of generating synthetic veiling glare images necessary for training and (ii) studying metrics used in related image enhancement tasks (dehazing and underwater image enhancement). We select three such no-reference metrics (UCIQE, UIQM and CCF) and show that their improvement indicates better veil removal. Finally, we experiment on neural network architectures and propose a two-branched architecture and a training procedure utilizing structural similarity measure.

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