The majority of common cameras have an integrated flash that improves lighting in a variety of situations, particularly in low-light environments. Before capturing an image, the photographer must make a decision regarding the usage of flash. However, flash strength cannot be adjusted once it has been utilised in an image. In this work, we target two application scenarios in computational flash photography: decomposition of a flash photograph into its illumination components and generating the flash illumination from a given single no-flash photograph. Two distinct approaches based on image-to-image transfer and intrinsic decomposition with the use of convolutional neural networks are employed to address these tasks. An additional network boosts and upscales the estimated results to generate the final illuminations. Key advantages of our approach include the preparation of a large flash/no-flash dataset and presenting models based on state-of-the-art methods to address subtasks specific to our problem.
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Thesis advisor: Aksoy, Yağız
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