A Torch Without Light: Low-Light Imaging for Mobile phones
Dipak Bange1, Abhishek Gaikwad2, Tejas Gajare3, Aditya Khadse4, Swati Shinde5
1Dipak Bange, Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
2Abhishek Gaikwad, Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
3Tejas Gajare*, Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
4Aditya Khadse, Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
5Dr. Swati V. Shinde, Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2042-2047 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9557109119/2019©BEIESP | DOI: 10.35940/ijeat.A9557.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Photography used to be a hobby that required equipment such as a professional camera. Today, photography has evolved to be a daily activity conducted on an unprecedented scale due to the adoption of camera into smartphones. Mobile phone cameras are on the way to completely replace other forms of camera due to their portability and quality. Millions of images are captured on mobile devices across the globe. These images are clear and crisp. But all these images are captured in daylight. Images taken in low illumination essentially turn out to be too dark to be comprehensible. Research shows that current solutions to this problem work for dim to moderate light level but fail in extreme low light. There are certain problems involved with these techniques. Firstly, image denoising relies on image priors limiting the situations on what it will work on. Other deep learning techniques work on synthetic data and cannot be proficient on real data. Secondly, Low light image enhancement assumes that images already contain a good representation of scene content. This paper proposes to capture low illumination images and transform them to high quality images using end to end fully convolutional neural network trained on our data set of raw images shot in low aperture and their corresponding high aperture raw images. As an outcome, we will be able to transform images to high quality and identify objects. Index Terms – Introduction, Existing Methods, Dataset, Using Transfer Learning, Training, Results, Conclusion, Future Work, References
Keywords: Artificial Intelligence, Computer Vision, Convolution Neural Network, Low-light Photography.