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Gradient-Based Low-Light Image Enhancement

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Masayuki Tanaka
Masayuki Tanaka

Gradient-Based Low-Light Image Enhancement presented at ICCE2019

Science
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Masayuki Tanaka Takashi Shibata Masatoshi Okutomi Gradient-Based Low-Light Image Enhancement
Background 1 Most common mistake is blurring of moving objects or by camera-shake. Blurred image Dark image Increase shutter speed Sensitivity of the sensor has been improved. But, we still have dark images with fast shutter speed.
Low-Light Image Enhancement 2 Take photo under low-lightscene Proposed Image EnhancementLow-Light Image Enhancement is highly demanded. We propose a gradient- based low-light image enhancement.
Gradient-Based Image Processing 3 Gradient information Intensity information> Shibata, Gradient-Domain Image Reconstruction Framework with Intensity-Range and Base-Structure Constraints, CVPR2016 Important for human visual system Many gradient- based image processing applications have been proposed.
Gradient-based image processing 4 Gradient extraction Gradient manipulation Gradient integration Post processing Processing pipeline Input image Output image Drawback of Gradient-based image processing The intensity range of output image is unknown unless the integration is performed. The intensity range of the output image often exceeds the typical intensity range of [0,255]. We’ve gotten saturation in the output images. Image enhancement
Example of post-processing 5 Input image Processed image ×1.5 Intensity range of processed image exceeds [0,255] Rescaling Intensity clipping Proposed
Proposed Integration 6 Shibata, Gradient-Domain Image Reconstruction Framework with Intensity-Range and Base-Structure Constraints, CVPR2016 Gradient extraction Gradient manipulation Proposed integration Input image Output image Intensity range information 𝐹𝐹 𝑢𝑢 𝑥𝑥 = � � 𝑑𝑑=ℎ,𝑣𝑣 𝜕𝜕𝑑𝑑 𝑢𝑢 𝑥𝑥 − 𝑞𝑞𝑑𝑑 𝑥𝑥 2 𝑑𝑑𝑑𝑑 + G𝑅𝑅[𝑢𝑢(𝑥𝑥)] 𝑢𝑢 𝐱𝐱 𝑔𝑔𝑅𝑅(𝑢𝑢 𝐱𝐱 ) 𝑅𝑅 𝑚𝑚𝑚𝑚 𝑚𝑚 𝑅𝑅 𝑚𝑚𝑚𝑚𝑚𝑚 Intensity range We penalize if the intensity exceeds the intensity range.This cost function means to preserving the gradient information while keeping the intensity range within the given intensity range.
Gradient Enhancement Strategy 7 Gradient extraction Gradient manipulation Proposed integration Input image Output image Intensity range information Intensity Gradient manipulation: Higher gain for darker region Input gradient Gain generation Enhanced gradient Intensity Gain β:15 1 τ:50
Experimental Comparisons 8 Input image Histogram equalization Automatic tone correction by photoshop HDR tone mapping by matlab LIME (Guo2016) Proposed
Experimental Comparisons 9 Input image Histogram equalization Automatic tone correction by photoshop HDR tone mapping by matlab LIME (Guo2016) Proposed
Experimental Results 10 Input images
Experimental Results 11 Proposed results
Real Time Demonstration 12
Conclusions 13 We propose have a gradient-based low-light image enhancement. • Higher gain for darker region • Image integration with intensity range constraint Input image Proposed result matlab code is available on our project page: http://www.ok.sc.e.titech.ac.jp/res/IC/LowLight/

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Gradient-Based Low-Light Image Enhancement