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Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement by Kar, Aupendu; Dhara, Sobhan K.; Sen, Debashis; Biswas, Prabir K. is a scholarly article available to read on EtoBox.

What is Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement about?

Real-world low-light images captured by imaging devices suffer from poor visibility and require a domain-specific enhancement to produce artifact-free outputs that reveal details. In this paper, we propose an unpaired low-light image enhancement network leveraging novel controlled transformation-based self-supervision and unpaired self-conditioning strategies. The model determines the required degrees of enhancement at the input image pixels, which are learned from the unpaired low-lit and well-lit images without any direct supervision. The self-supervision is based on a controlled transformation of the input image and subsequent maintenance of its enhancement in spite of the transformation. The self-conditioning performs training of the model on unpaired images such that it does not enhance an already-enhanced image or a well-lit input image. The inherent noise in the input low-light images is handled by employing low gradient magnitude suppression in a detail-preserving manner. In addition, our noise handling is self-conditioned by preventing the denoising of noise-free well-lit images. The training based on low-light image enhancement-specific attributes allows our model to avoi

Author

Kar, Aupendu; Dhara, Sobhan K.; Sen, Debashis; Biswas, Prabir K.

Published

2025

Language

EN