用轻量级自编码器同时提升暗光图像亮度与清晰度
DeepFusionNet: Autoencoder-Based Low-Light Image Enhancement and Super-Resolution
- 基于自编码器架构融合低光增强与超分辨率
- 在LOL-v1数据集上实现92.8% SSIM、26.30 PSNR
- 模型仅250万参数，适合实时图像处理场景
计算机视觉与图像处理应用常受暗光或低照度图像影响，尤其在实时传输中。现有方法多采用自编码器将暗图转为明亮彩色图像，但普遍存在结构复杂、参数量大、计算开销高，且导致较低的SSIM与PSNR。为此，本文提出DeepFusionNet架构，在LOL-v1数据集上实现92.8%的SSIM和26.30的PSNR，参数量仅约250万。此外，针对模糊低分辨率图像，开发了基于DeepFusionNet的自编码器超分辨率模型，参数量约10万，测试验证集上取得25.30 PSNR和80.7% SSIM，优于传统GAN方法。
原文摘要 · Abstract (English)
Computer vision and image processing applications suffer from dark and low-light images, particularly during real-time image transmission. Currently, low light and dark images are converted to bright and colored forms using autoencoders; however, these methods often achieve low SSIM and PSNR scores and require high computational power due to their large number of parameters. To address these challenges, the DeepFusionNet architecture has been developed. According to the results obtained with the LOL-v1 dataset, DeepFusionNet achieved an SSIM of 92.8% and a PSNR score of 26.30, while containing only approximately 2.5 million parameters. On the other hand, conversion of blurry and low-resolution images into high-resolution and blur-free images has gained importance in image processing applications. Unlike GAN-based super-resolution methods, an autoencoder-based super resolution model has been developed that contains approximately 100 thousand parameters and uses the DeepFusionNet architecture. According to the results of the tests, the DeepFusionNet based super-resolution method achieved a PSNR of 25.30 and a SSIM score of 80.7 percent according to the validation set.
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