Abstract
Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.
Highlights
Several hardware- and software-based solutions are enhancing the quality of images captured with smartphone cameras
We generated our own dataset consisting of 44 images having a variety of different lighting conditions, including low light images, high dynamic range scenes, properly exposed scenes, and extreme lighting conditions similar to the VV dataset [54]
We compared our methodology to Contrast limited adaptive histogram equalization (CLAHE) [2], AGCWD [5], segmentation-based luminance adjustment (SSLA) [12], GLF [3], and Bio-inspired Multiexposure fusion framework (BioMEF) [13]
Summary
Several hardware- and software-based solutions are enhancing the quality of images captured with smartphone cameras. Neural network- (NN-) based techniques [7, 8] have been developed, which perform specific image enhancement tasks These techniques have some intrinsic limitations, including slow processing speed and huge memory requirements. Exposure fusion [9, 10] is yet another technique that improves the quality of an image by combining multiple low dynamic range images of varying exposures and fuses the best parts of each image. This technique introduces artifacts in the presence of motion blur in the image stack. There is no single method for creating high-quality, artifact-free images
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