# Frequency Domain Information Integrated Network for Low-Light Image Enhancement

Na Li, Xi Luo, Dunlu Peng, Zied Bouraoui

► To cite this version:

Na Li, Xi Luo, Dunlu Peng, Zied Bouraoui. Frequency Domain Information Integrated Network for Low-Light Image Enhancement. 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2025), Apr 2025, Hyderabad, India. 10.1109/ICASSP49660.2025.10890113. hal-05265535

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Distributed under a Creative Commons Attribution 4.0 International License
# Frequency Domain Information Integrated Network for Low-Light Image Enhancement

Na Li

USST

Shanghai, China
li_na@usst.edu.cn

Xi Luo

USST

Shanghai, China
232340568@st.usst.edu.cn

Dunlu Peng

USST

Shanghai, China
pengdl@usst.edu.cn

Zied Bouraoui

CRIL CNRS & Univ. Artois

Lens, France
bouraoui@cril.fr

**Abstract**—Low-light images often suffer from significant noise and detail loss, making it challenging to effectively distinguish signals from noise when processed directly in the spatial domain. To this end, we introduce frequency domain information to better distinguish high-frequency details from low-frequency components, thereby suppressing noise while enhancing details. The proposed method converts sRGB images into raw-RGB images by reversing the Image Signal Processor (ISP) pipeline to avoid unnecessary effects. A frequency information interaction processing unit is then designed to enhance low-frequency textures using high-frequency information and correct high-frequency data through low-frequency information to reduce structural deformation during restoration. The signal-to-noise ratio prior is introduced in the hidden layer to further remove noise. Experimental results show that the proposed method outperforms existing color and structure enhancement methods.

**Index Terms**—Low-light image enhancement, Fourier frequency information, Denoising

## I. INTRODUCTION

Low-Light Image Enhancement (LLIE) focuses on improving the quality of images captured under insufficient lighting, aiming to enhance illumination and visual clarity. This enhancement is crucial for a wide range of downstream applications, including object detection, face recognition, and image segmentation. Images taken in poor lighting conditions often suffer from challenges such as color distortion, excessive noise, and loss of detail, making LLIE a complex and significant problem in computer vision.

Recent advancements in LLIE have been driven by models based on CNNs [1] and transformer-based architectures [2, 3], which have demonstrated notable progress. However, these methods often emphasize the spatial domain, overlooking the crucial role of frequency domain information. Recent studies [4, 5] have underscored the importance of incorporating frequency domain data in image processing, highlighting how low-frequency components capture the overall structure, while high-frequency components preserve fine details.

Building on these findings, we propose FDINet a Frequency Domain Information Integrated Network, which takes advantage of the complementary characteristics and mutual enhancement of high- and low-frequency information. Our approach is designed to enhance both image quality and detail restoration through a two-stage process. First, we perform color space conversion and apply preliminary enhancements to

illumination and color. We refer to this process as appearance modeling. The second stage focuses on frequency domain information modeling, addressing the degradation of low-frequency information, which is more pronounced in low-light conditions. To achieve this, we separate the image into high- and low-frequency components and enhance them using a frequency information interaction processing unit. This unit initially uses high-frequency data to improve low-frequency components, and the enhanced low-frequency information is then used to refine the high-frequency details.

The main contributions of this paper are as follows:

* We propose a two-stage framework, which initially restores brightness and color through appearance modeling and then promotes the enhancement of structural details through the guidance of frequency domain information.

* We design a frequency information interaction processing unit that complements the high- and low-frequency information and restores the more accurate texture details.

* We conducted extensive experiments on various datasets to demonstrate the effectiveness of our method.

## II. FDINET MODEL

We introduce FDINet architecture for enhanced image processing, which is structured in two key stages: *Appearance Modeling* and *Frequency Domain Information Modeling*. Figure 1 provides a comprehensive overview of FDINet, illustrating how these stages interact to enhance image quality.

### A. Appearance Modeling

The Appearance Modeling stage focuses on refining color and illumination, laying the groundwork for visually appealing results. We perform two tasks: converting the images from sRGB to raw RGB and performing preliminary color and illumination enhancement.

Raw RGB images, being scene-referred, more accurately represent the true colors of a scene compared to sRGB images, which are display-referred and processed by the camera's ISP. Recent LLIE methods leverage raw RGB inputs for this reason. To utilize this advantage, we first convert sRGB images into raw RGB format. Inspired by [6], we apply a pixel-level transformation $I_{raw} = I_{low} \odot M + A$ to the input image $I_{low}$ to obtain the raw RGB image $I_{raw}$. To efficiently learn the multiplication matrix $M$ and the addition matrix $A$, we use
<figure><img src="image_2.png" /><figcaption>Fig. 1. The overall architecture of the model consists of two main stages: appearance modeling and frequency information modeling. A frequency information interaction processing unit (FIIPU) is placed after each decoder layer.</figcaption></figure>

NAF blocks [7] since they are computationally efficient and perform better.

Next, we enhance the color and illumination (CIRB in Fig. 1) of the processed images through a color transformation and gamma correction. Specifically, we learn a 3 × 3 color matrix $W_{color}$ which transforms the processed raw RGB images to a better color space using a dot product.

$$I_{color} = W_{color} \cdot I_{raw}$$

Following the color transformation, we apply a standard gamma curve to simulate the non-linear perception of brightness by the human eye. The final image $I_a$ after appearance modeling can be obtained by performing the following gamma transformation on each pixel of $I_{color}$:

$$I_a^i = \max(I_{color}^i, \epsilon)^\gamma$$

where $I_a^i$ indicate i-th pixel, $\gamma$ is a learnable parameter and $\epsilon = 10^{-8}$ is a non-negative number close to 0. As shown in Fig. 1(c), both color matrix $W_{color}$ and the parameter $\gamma$ are learned by the cross-attention mechanism.

## B. Frequency Domain Information Modeling

The frequency domain information modeling aims to refine textural details and perform denoising. As illustrated in Fig. 1 (b), we adopt an encoder-decoder architecture. The encoder consists of four Double Convolution Blocks, each composed of two 3×3 convolution layers followed by a batch normalization (BN) layer and ReLU activation function. The decoder mirrors the encoder’s structure but integrates a Frequency Information Interaction Processing Unit (FIIPU) after each convolution block. The FIIPU is specially designed to facilitate the interaction between high- and low-frequency information. Additionally, a denoising network is placed between the encoder

and the decoder. The details of the FIIPU and the denoising network are introduced below.

### a) Frequency Information Interaction Processing Unit:
The inputs to the FIIPU include the image $I_a$, obtained after appearance modeling, and the feature map $F$, processed by the preceding convolutional blocks. The FIIPU can be divided into two steps: information decoupling and information interaction.

**Information Decoupling.** The goal of information decoupling is to effectively separate high-frequency and low-frequency information. To initiate this process, we first pass the image $I_a$ through the 3 × 3 convolutional layer and then apply Fast Fourier Transform to the output features $F_c$, obtaining the spectrum $F_{ref}$. To achieve the decoupling, we introduce an adaptive mask generator (MG) that dynamically produces masks based on the size of the image, which are then used to divide the spectrum $F_{ref}$. The MG learns two parameters, $h$ and $w$, representing the height and width of the low-frequency mask, which are adaptively scaled according to the size of the image. The specific generation process is shown below:

$$(h, w) = \text{sigmoid}(\text{Conv}_{1 \times 1}(\varphi(\text{Conv}_{1 \times 1}(\text{AP}(F_c))))))$$

where $\text{Conv}_{1 \times 1}$ represents 1 × 1 convolution, $\varphi$ is the GELU activation function, and AP represents global average pooling.

Based on the learned parameters $h$ and $w$, we set two binary mask matrices with the same size as the image, representing low- and high-frequency masks, respectively. Specifically, the low-frequency mask $L_{mask}$ is obtained by setting the values of the region $[\frac{H}{2} - h : \frac{H}{2} + h, \frac{W}{2} - w : \frac{W}{2} + w]$ to 1. The high-frequency mask is simply the opposite of the low-frequency mask, with the remaining region set to 1. These masks are then used to extract high- and low-frequency information of $F_{ref}$,
allowing the decoupling of the spatial domain. The information extraction process is expressed as:

$$ Fre_{high} = Fre_{f} \odot H_{mask}, Fre_{low} = Fre_{f} \odot L_{mask} $$

where $Fre_{high}$ refers to high-frequency information, and $Fre_{low}$ to the low-frequency information, $\odot$ denotes element-wise multiplication.

To complete the decoupling of the spatial domain, we apply channel cross-attention, with the guidance of $Fre_{high}$ and $Fre_{low}$. The frequency information $Fre_{high}$ and $Fre_{low}$ are processed by the Inverse Fast Fourier Transform (IFFT) and two convolutions, as queries $Q_h$ and $Q_l$. The feature map $F$ from previous convolutions is used as key $K$ and value $V$. The process is as follows:

$$ Q_h = \text{Conv}_{3 \times 3}(\text{Conv}_{1 \times 1}(\text{IFFT}(Fre_{high}))) $$

$$ Q_l = \text{Conv}_{3 \times 3}(\text{Conv}_{1 \times 1}(\text{IFFT}(Fre_{low}))) $$

$$ X_{high} = \text{Softmax}(Q_h K^T / \alpha) V $$

$$ X_{low} = \text{Softmax}(Q_l K^T / \alpha) V $$

where $\alpha$ is a learnable scaling parameter to control the magnitude of the dot product of $K$ and $Q$, $X_{low}$ and $X_{high}$ are the low- and high-frequency information minded from the spatial domain.

**Information Interaction.** Information interaction aims to enable high-frequency and low-frequency information components to complement one another effectively. First, we apply spatial attention on $X_{high}$ to better capture texture details and use this information to enhance low-frequency information $X_{low}$. The updated low-frequency information $X_{low}^*$ is then leveraged to obtain the global image features via channel attention, which, in turn, allows to refine the high-frequency details. Finally, the enhanced low- and high-frequency components are fused to produce the high-quality output features $F_{ii}$. This process ensures a balance between preserving global structural integrity and restoring fine textures, crucial for high-quality image enhancement. The interaction process is as follows:

$$ A_h = SA(X_{high}), X_{low}^* = X_{low} \odot (A_h) $$

$$ A_l = CA(X_{low}^*), X_{high}^* = X_{high} \odot (A_l) $$

$$ F_{ii} = \text{Conv}_{1 \times 1}(X_{low}^* \oplus X_{high}^*) $$

where $A_l$ and $A_h$ represent the low- and high-frequency attention map respectively, $CA$ represents channel attention, $SA$ represents spatial attention, $\oplus$ represents element-by-element addition.

b) **Denoising Network:** To further enhance image quality, we introduce a denoising network between the encoder and decoder to mitigate the impact of noise, which is particularly relevant in low-light environments. Inspired by [8], we incorporate an attention mechanism that focuses on regions with high signal-to-noise ratio (SNR) and ignores areas with high noise. The SNR map is calculated as follows:

$$ N = |I_g - \hat{I}_g|, S = \hat{I}_g/N $$

Where $I_g$ is the grayscale image, and $\hat{I}_g$ is the denoised image. The denoising network consists of a transformer block and a

residual convolution block and uses the SNR map to ignore noisy areas when calculating attention.

### C. Training Objectives

Our training approach employs a combination of multiple loss functions to ensure both accuracy and quality of the restored images. Specifically, we utilize smooth L1 loss, perceptual loss [9], color loss [10], and gradient loss, each targeting different aspects of image restoration. Perceptual loss is particularly effective in improving visual quality.

$$ L_{pcep} = \frac{1}{N} \sum_{i=0}^{N} (\Phi(I')_i - \Phi(\hat{I})_i)^2 $$

where $\Phi$ is the operation of extracting features from VGG, $\hat{I}$ is the output image, $I'$ is the real image, and $i$ is the feature index.

To prevent the restored image from producing obvious color distortion, we further introduce the color loss, which is expressed as:

$$ L_{color} = \sum_{i} \cos (I'_{i}, \hat{I}_{i}) $$

where $i$ is the pixel index, and $\cos$ refers to the cosine similarity between the restored image and the real image.

To prevent the denoising and other operations from causing negative effects such as blurring artifacts in the restored images, we introduce gradient loss:

$$ L_{grad} = \left\| \nabla x I' - \nabla x \hat{I} \right\|_{1} - \left\| \nabla y I' - \nabla y \hat{I} \right\|_{1} $$

where $\nabla x, \nabla y$ represent the gradient operators along the horizontal and vertical directions. The overall objectives with $\lambda_1$ and $\lambda_2$ as the weights is:

$$ I_{total} = L_{smooth} + \lambda_1 L_{vgg} + \lambda_2 L_{color} + L_{grad} $$

This combination of losses ensures that the network not only reduces pixel-level errors but also enhances perceptual quality, preserves accurate color representation, and maintains structural sharpness.

## III. EXPERIMENTS

We evaluate the performance of FDINet on three paired datasets: LOL-Real [11], LSRW-Nikon [12] and MIT [13], as well as four unpaired datasets: DICM [14], LIME [15], MEF [16] and NPE [17]. Images are captured under diverse lighting and scene conditions, allowing us to evaluate the model's generalization and robustness. All experiments were performed on an NVIDIA Tesla T4 GPU. We initialized the learning rate at $2.0 \times 10^{-4}$, with hyperparameters $\lambda_1$ and $\lambda_2$ set to 0.04 and 0.5 respectively. An Adam optimizer was used, with a batch size of 4 and a total of 200 iterations.

### A. Comparison with State-Of-The-Arts

We compare the performance of FDINet against the state-of-the-art LLIE methods as baselines, including the unsupervised learning methods Zero-DCE [1], EnlightGAN [18], and the supervised learning methods KinD [19], SGM [20], MIRNet [21], FECNet [22] and FourLLIE [23].
<figure><img src="image_3.png" /><figcaption>Fig. 2. Visual comparisons on LOL-real. Our FDINet has less noise and clearer visibility.</figcaption></figure>

<table><caption>TABLE I
PERFORMANCE COMPARISON BETWEEN SUPERVISED AND UNSUPERVISED METHODS.</caption><thead><tr><th rowspan="2">Method</th><th colspan="2">LOL-real</th><th colspan="2">MIT</th><th colspan="2">LSRW</th></tr><tr><th>PSNR</th><th>SSIM</th><th>PSNR</th><th>SSIM</th><th>PSNR</th><th>SSIM</th></tr></thead><tbody><tr><td>Zero-DCE</td><td>16.06</td><td>0.580</td><td>17.40</td><td>0.798</td><td>11.23</td><td>0.235</td></tr><tr><td>EnlighenGAN</td><td>18.23</td><td>0.617</td><td>15.91</td><td>0.779</td><td>12.85</td><td>0.287</td></tr><tr><td>KinD</td><td>20.01</td><td>0.841</td><td>16.86</td><td>0.818</td><td>11.52</td><td>0.382</td></tr><tr><td>SGM</td><td>20.06</td><td>0.815</td><td>23.61</td><td>0.869</td><td>15.73</td><td>0.497</td></tr><tr><td>MIRNet</td><td>22.11</td><td>0.794</td><td>24.18</td><td>0.832</td><td>17.10</td><td>0.502</td></tr><tr><td>FECNet</td><td>20.67</td><td>0.795</td><td>23.09</td><td>0.811</td><td>17.82</td><td>0.499</td></tr><tr><td>CUE</td><td>21.67</td><td>0.774</td><td>24.86</td><td><u>0.872</u></td><td><strong>18.67</strong></td><td><u>0.538</u></td></tr><tr><td>FourLLIE</td><td><u>22.34</u></td><td><strong>0.846</strong></td><td><u>26.31</u></td><td>0.863</td><td>17.82</td><td>0.504</td></tr><tr><td>FDINet</td><td><strong>23.20</strong></td><td><u>0.835</u></td><td><strong>28.07</strong></td><td><strong>0.913</strong></td><td><u>18.32</u></td><td><strong>0.587</strong></td></tr></tbody></table>

<table><caption>TABLE II
SCORES OF DICM, LIME, MEF AND NPE ON NIQE. THE SMALLER THE BETTER.</caption><thead><tr><th>Method</th><th>LIME</th><th>DICM</th><th>NPE</th><th>MEF</th></tr></thead><tbody><tr><td>KinD</td><td>4.772</td><td>3.614</td><td>4.175</td><td>4.819</td></tr><tr><td>SGM</td><td>5.451</td><td>4.733</td><td>5.208</td><td>5.754</td></tr><tr><td>MIRNet</td><td>6.453</td><td>4.042</td><td>5.235</td><td>5.504</td></tr><tr><td>FECNet</td><td>6.041</td><td>4.139</td><td>4.500</td><td>4.707</td></tr><tr><td>FourLLIE</td><td><strong>4.402</strong></td><td>3.374</td><td>3.909</td><td>4.362</td></tr><tr><td>FDINet</td><td>4.413</td><td><strong>3.116</strong></td><td><strong>3.902</strong></td><td><strong>4.089</strong></td></tr></tbody></table>

1) *Quantitative Evaluation:* Table I reports the average PSNR/SSIM scores on the paired datasets. FDINet consistently achieves superior performance across these metrics. In particular, FDINet is nearly 3dB higher in the PSNR than FourLLIE on the MIT dataset. FDINet also has competitive results in terms of SSIM on the LOL-real dataset and PSNR on the LSRW dataset. Table II reports the Naturalness Image Quality Evaluator (NIQE) scores of the unpaired datasets. Lower NIQE scores represent higher naturalness image quality. FDINet outperforms existing LLIE methods on almost all datasets.

<table><caption>TABLE III
ABLATION EXPERIMENT ON LOL-REAL, THE BEST RESULTS ARE SHOWN IN BOLD.</caption><thead><tr><th>Method</th><th>AP</th><th>FIIPU</th><th>DN</th><th>PNSR</th><th>SSIM</th></tr></thead><tbody><tr><td>Setting1</td><td>✓</td><td></td><td>✓</td><td>21.75</td><td>0.781</td></tr><tr><td>Setting2</td><td></td><td>✓</td><td>✓</td><td>22.68</td><td>0.826</td></tr><tr><td>Setting3</td><td>✓</td><td>✓</td><td></td><td>22.39</td><td>0.829</td></tr><tr><td>FDINet</td><td>✓</td><td>✓</td><td>✓</td><td><strong>23.20</strong></td><td><strong>0.835</strong></td></tr></tbody></table>

2) *Qualitative Comparison:* Fig. 2 presents a vision comparison of several representative methods on LOL-real. It can be seen that our FDINet shows the best visual results, which have less noise and clearer details and visibility.

## B. Ablation Study

FDINet enhances low-light images through two stages: appearance modeling and frequency information modeling. We perform an ablation study, examining the impact of removing individual elements from the model: (Setting1) removing the FIIPU, (Setting2) removing Appearance modeling, and (Setting3) removing the Denoising network. Table III shows the results on the LOL-real dataset. The full FDINet configuration achieves the highest PSNR and SSIM scores compared to the ablation settings, validating the importance of each component.

## IV. CONCLUSION

We introduced FIDNet a two-stage restoration framework for LLIE that first restores illumination and color through appearance modeling and then refines texture details using frequency domain information. Our Frequency Domain Information Interaction Unit enhances the synergy between high- and low-frequency information in the spatial domain. Experimental results show that our FDINet is superior to other competing methods.

## V. ACKNOWLEDGMENTS

This work was supported by ANR-22-CE23-0002 ERIANA.
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