Abstract:
"
The images capture in poor lighting conditions is called low light images. They can be
varying from absolute dark to twilight lightning conditions. low contrast, color
distortion, and significant noise are the main characteristics of those images.
With modern hardware, it is not an easy task to take images in dark environments. It
was found that the general camera and mobile users were unable to capture images in
those low light conditions. The research is carried out to address that issue with a deep
learning approach. TARSIERS an alternative approach to taking images in low light
conditions by taking images in normal light settings and enhancing them afterward.
The proposed model can automatically enhance those images by estimating image specific curves. The model consists of two-loss function pathways, for the original
image and its inverted. The carefully designed model has feature attention clocks of
color attention and pixel attention blocks to keep images' important details remain
during the enhancement process. Influenced by unsupervised learning, the model does
not require the paired or unpaired image for training. The product component consists
of a web app that can be used to upload images and to change image properties. This
approach, even the low-end mobile devices can use to get the advantages of
computational photography.
TARSIERS lightweight image enhancement model is capable of enhancing low light
images without introducing noise and preserving image details. In testing phase model
performed 500 fps in the range of 500px to 1000px. The modularized system is also
capable of act as a middle layer for other computer vision tasks. This was tested with
low light face recognition.
The source code, trained model, and dataset will be published as an open-source
project after the research marking was completed. The future enhancement will be
carried out video enhancement and usage of image segmentation to enhance high resolution images.
This abstract was submitted to IESL Young Members Section Technical Conference
2021.
"
