Low-Light Image Enhancement and Noise Suppression Using Disentangled Frequency-Guided Processing with Progressive Graph Convolutional Networks
TL;DR
This manuscript proposes enhancing visibility and reducing noise in lowlight images through disentangled frequency-guided processing and progressive graph convolutional networks (LLI-DFP-PGCN), which attains high PSNR, and high SSIM compared to existing techniques.
Abstract
Enhancing visibility and reducing noise in low-light images significantly improves the usability of images in surveillance, medical imaging, and autonomous driving, where clarity in poor lighting is crucial for accurate interpretation. Many low-light image enhancement approaches struggle to effectively balance brightness enhancement and detail preservation, often resulting in either overexposed regions or loss of fine details. To overcome this issue, this manuscript proposes enhancing visibility and reducing noise in lowlight images through disentangled frequency-guided processing and progressive graph convolutional networks (LLI-DFP-PGCN). Initially, input images are gathered from the Smartphone Image Denoising Dataset (SIDD) and undergo initial enhancement using the Confidence Partitioning Sampling Filtering (CPSF) method, which improves visibility by selecting high-confidence pixel regions, filtering noise, and guiding the enhancement process. The enhanced images are then processed using the Advanced Disentangled Frequency Paradigm for Low-Light Image Enhancement (AFD-LLIE), which separates lower and higher frequency components of an image to independently enhance illumination and preserve fine details. Finally, the frequency refined images are passed to the Progressive Graph Convolutional Networks (PGCN), which models complex spatial-frequency relationships to restore realistic colour, preserve texture, and eliminate residual noise. The performance of the proposed LLI-DFP-PGCN approach is evaluated using Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS), Natural Image Quality Evaluator (NIQE), Mean Absolute Error (MAE) and Computational time. The proposed approach attains high PSNR, and high SSIM compared to existing techniques like Denoising diffusion post-processing for lower-light image enhancement (DDPLLIE-CNN), a Self-supervised network for low-light traffic image enhancement depending on deep noise and artifacts removal (LTIE-DNAR-GAN) and A Joint Network for Low-Light Image Enhancement Depending upon Retinex (LLIEDCNN) methods respectively.