scholarly journals Low-Light Image Enhancement by Principal Component Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 3082-3092 ◽  
Author(s):  
Steffi Agino Priyanka ◽  
Yuan-Kai Wang ◽  
Shih-Yu Huang
Keyword(s):  
Principal Component Analysis ◽  
Image Enhancement ◽  
Principal Component ◽  
Component Analysis ◽  
Low Light ◽  
Light Image

scholarly journals Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features

2018 ◽  
Vol 8 (8) ◽  
pp. 1321 ◽  
Author(s):  
Minseo Kim ◽  
Soohwan Yu ◽  
Seonhee Park ◽  
Sangkeun Lee ◽  
Joonki Paik
Keyword(s):  
Principal Component Analysis ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Visual Similarity ◽  
Computationally Efficient ◽  
Low Light ◽  
Image Dehazing ◽  
Haze Removal ◽  
Transmission Estimation

This paper presents a computationally efficient haze removal and image enhancement methods. The major contribution of the proposed research is two-fold: (i) an accurate atmospheric light estimation using principal component analysis, and (ii) learning-based transmission estimation. To reduce the computational cost, we impose a constraint on the candidate pixels to estimate the haze components in the sub-image. In addition, the proposed method extracts modified haze-relevant features to estimate an accurate transmission using random forest. Experimental results show that the proposed method can provide high-quality results with a significantly reduced computational load compared with existing methods. In addition, we demonstrate that the proposed method can significantly enhance the contrast of low-light images according to the assumption on the visual similarity between the inverted low-light and haze images.

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