Global Structure-Aware Diffusion Process for Low-light Image Enhancement
Authors: Jinhui HOU, Zhiyu Zhu, Junhui Hou, Hui LIU, Huanqiang Zeng, Hui Yuan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations reveal that the proposed diffusion-based framework, complemented by rank-informed regularization, attains distinguished performance in low-light enhancement. The outcomes indicate substantial advancements in image quality, noise suppression, and contrast amplification in comparison with state-of-the-art methods. We believe this innovative approach will stimulate further exploration and advancement in low-light image processing, with potential implications for other applications of diffusion models. The code is publicly available at https://github.com/jinnh/GSAD. 4 Experiments 4.1 Experiment Settings 4.2 Comparison with State-of-the-Art Methods 4.3 Ablation Study |
| Researcher Affiliation | Academia | Jinhui Hou1, Zhiyu Zhu1, Junhui Hou1 , Hui Liu2, Huanqiang Zeng3, and Hui Yuan4 1City University of Hong Kong, 2Caritas Institute of Higher Education 3Huaqiao University, 4Shandong University |
| Pseudocode | Yes | Algorithm 1 Forward process 1: Repeat 2: X0 q(X0) 3: t Uniform({1, 2, ..., T}) 4: ϵ N(0, I) 5: Take gradient descent step on 6: θ ϵ ϵθ( αt X0 + 1 αtϵ, t) 2 7: Until converged Algorithm 2 Reverse process 1: XT N(0, I) 2: For t = T, ..., 1 do 3: z N(0, I) if t > 1, else z = 0 4: Xt 1 = 1 αt (Xt 1 αt 1 αt ϵθ(Xt, t))+σtz 5: End for 6: Return X0 Algorithm 3 Training a diffusion-based low-light enhancement model Require: Normal and low-light image pairs (X0, Y), and a pre-trained models µθ1 and ϵˆθ. 1: Initialize parameters of µθ1( ) (resp. ϵθ( )) from pretrained ˆθ1 (resp. ˆθ), and freeze ˆθ1. 2: Repeat 3: Sample an image pair (X0, Y), αt p( α) and ϵ N(0, I) 4: Xt = αt X0 + 1 αt ϵ 5: Pt µθ1(Y, Xt, αt) 6: Construct the learnable closed-form sample Xt 1 via Eq. (2) 7: Perform a gradient descent step on 8: θ{λ Pt ϵ Pt ϵθ(Y, Xt, αt) 1 + Lt s }, where λ is a balancing factor, and Lt s refers to Eq. (4) 9: Until converged 10: Return the resulting diffusion model ϵ θ |
| Open Source Code | Yes | The code is publicly available at https://github.com/jinnh/GSAD. |
| Open Datasets | Yes | We employed seven commonly-used LLIE benchmark datasets for evaluation, including LOLv1 [45], LOLv2 [54], DICM [11], LIME [6], MEF [14], NPE [39], and VV2. Specifically, LOLv1 contains 485 low/normal-light image pairs for training and 15 pairs for testing, captured at various exposure times from the real scene. LOLv2 is split into two subsets: LOLv2-real and LOLv2-synthetic. LOLv2-real comprises 689 pairs of low-/normal-light images for training and 100 pairs for testing, collected by adjusting the exposure time and ISO. LOLv2-synthetic was generated by analyzing the illumination distribution of low-light images, consisting of 900 paired images for training and 100 pairs for testing. |
| Dataset Splits | No | The paper specifies training and testing splits for datasets but does not explicitly mention a dedicated validation dataset or split for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions "Py Torch" and "Adam optimizer" but does not specify their version numbers or any other software dependencies with versions. |
| Experiment Setup | Yes | We trained our models for 2M iteration steps with Py Torch. We employed an Adam optimizer [10] with a fixed learning rate of 1 10 4 without weight decay. We applied an exponential moving average with a weight of 0.9999 during parameter updates. During training, we utilized a fine-grained diffusion process with T = 500 steps and a linear noise schedule with the two endpoints of 1 10 4 and 2 10 2. The patch size and batch size were set to 96 and 8, respectively. The hyper-parameter λ was empirically set to 10. |