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- W4310007306 abstract "Brightness enhancement aims to enhance the brightness of dark or low-light images in order to improve quality and visibility. Numerous research studies have been carried out to increase the brightness of low-light images. However, the majority of the study focuses on modern high-quality low-light images only. Old photos are dark, noisy, and degraded compared to normal modern images. Using state-of-the-art networks trained on modern low-light images to improve the brightness of old images leads in color-distorted, overexposed output due to the characteristics of old images. In this paper, a deep convolutional neural network is proposed to perform the brightness enhancement of old photos. The proposed network is a combination of an illumination network and a deep network that estimates best-fitting light enhancement curves for a given input image. The combination of the illumination network and curve estimation network do better brightness and color enhancement resulting in more natural output. According to experimental findings, the suggested technique works better than the conventional methods for brightness enhancement on old image datasets in terms of visual quality." @default.
- W4310007306 created "2022-11-30" @default.
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- W4310007306 date "2022-10-19" @default.
- W4310007306 modified "2023-09-25" @default.
- W4310007306 title "Estimating Deep Curve and Illumination Maps for Old Image Brightness Enhancement" @default.
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- W4310007306 doi "https://doi.org/10.1109/ictc55196.2022.9952417" @default.
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