Enhancing night photography images is a challenging task that requires advanced processing techniques. While CNN-based methods have shown promising results, their high computational requirements and limited interpretability can pose challenges. To address these limitations, we propose a camera pipeline for rendering visually pleasing photographs in low-light conditions. Our approach is characterized by a shallow structure, explainable steps, and a low parameter count, resulting in computationally efficient processing. We compared the proposed pipeline with recent CNN-based state-of-the-art approaches for low-light image enhancement, showing that our approach produces more aesthetically pleasing results. The psycho-visual comparisons conducted in this work show how our proposed solution is preferred with respect to the other methods (in about 44% of the cases our solution has been chosen, compared to only about 15% of the cases for the state-of-the-art best method).
Zini, S., Rota, C., Buzzelli, M., Bianco, S., Schettini, R. (2023). Shallow Camera Pipeline for Night Photography Enhancement. In Image Analysis and Processing – ICIAP 2023 22nd International Conference, ICIAP 2023, Udine, Italy, September 11–15, 2023, Proceedings, Part I (pp.51-61). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-43148-7_5].
Shallow Camera Pipeline for Night Photography Enhancement
Simone Zini
;Claudio Rota;Marco Buzzelli;Simone Bianco;Raimondo Schettini
2023
Abstract
Enhancing night photography images is a challenging task that requires advanced processing techniques. While CNN-based methods have shown promising results, their high computational requirements and limited interpretability can pose challenges. To address these limitations, we propose a camera pipeline for rendering visually pleasing photographs in low-light conditions. Our approach is characterized by a shallow structure, explainable steps, and a low parameter count, resulting in computationally efficient processing. We compared the proposed pipeline with recent CNN-based state-of-the-art approaches for low-light image enhancement, showing that our approach produces more aesthetically pleasing results. The psycho-visual comparisons conducted in this work show how our proposed solution is preferred with respect to the other methods (in about 44% of the cases our solution has been chosen, compared to only about 15% of the cases for the state-of-the-art best method).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.