scholarly journals LISU: Low-light indoor scene understanding with joint learning of reflectance restoration

2022 ◽  
Vol 183 ◽  
pp. 470-481
Author(s):  
Ning Zhang ◽  
Francesco Nex ◽  
Norman Kerle ◽  
George Vosselman
Keyword(s):  
Scene Understanding ◽  
Low Light ◽  
Joint Learning ◽  
Indoor Scene

A search-classify approach for cluttered indoor scene understanding

2012 ◽  
Vol 31 (6) ◽  
pp. 1-10 ◽  
Author(s):  
Liangliang Nan ◽  
Ke Xie ◽  
Andrei Sharf
Keyword(s):  
Scene Understanding ◽  
Indoor Scene

scholarly journals Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1859-1887 ◽  
Author(s):  
Muzammal Naseer ◽  
Salman Khan ◽  
Fatih Porikli
Keyword(s):  
Autonomous Agents ◽  
Scene Understanding ◽  
Indoor Scene

scholarly journals Low Light Image Enhancement using Convolutional Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 3463-3472
Author(s):  
Choundur Vishnu
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Scene Understanding ◽  
Object Location ◽  
Low Light ◽  
Light Image ◽  
Image Improvement

Great quality images and pictures are remarkable for some perceptions. Nonetheless, not each and every images are in acceptable features and quality as they are capture in non-identical light atmosphere. At the point when an image is capture in a low light state the pixel esteems are in a low-esteem range, which will cause image quality to decrease evidently. Since the entire image shows up dull, it's difficult to recognize items or surfaces clearly. Thus, it is vital to improve the nature of low-light images. Low light image enhancement is required in numerous PC vision undertakings for object location and scene understanding. In some cases there is a condition when image caught in low light consistently experience the ill effects of low difference and splendor which builds the trouble of resulting undeniable level undertaking in incredible degree. Low light image improvement utilizing convolutional neural network framework accepts dull or dark images as information and creates brilliant images as a yield without upsetting the substance of the image. So understanding the scene caught through image becomes simpler task.

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