Loss Of Detail Research Articles

CrystalNet: Texture‐Aware Neural Refraction Baking for Global Illumination

AbstractNeural rendering bakes global illumination and other computationally costly effects into the weights of a neural network, allowing to efficiently synthesize photorealistic images without relying on path tracing. In neural rendering approaches, G‐buffers obtained from rasterization through direct rendering provide information regarding the scene such as position, normal, and textures to the neural network, achieving accurate and stable rendering quality in real‐time. However, due to the use of G‐buffers, existing methods struggle to accurately render transparency and refraction effects, as G‐buffers do not capture any ray information from multiple light ray bounces. This limitation results in blurriness, distortions, and loss of detail in rendered images that contain transparency and refraction, and is particularly notable in scenes with refracted objects that have high‐frequency textures. In this work, we propose a neural network architecture to encode critical rendering information, including texture coordinates from refracted rays, and enable reconstruction of high‐frequency textures in areas with refraction. Our approach is able to achieve accurate refraction rendering in challenging scenes with a diversity of overlapping transparent objects. Experimental results demonstrate that our method can interactively render high quality refraction effects with global illumination, unlike existing neural rendering approaches. Our code can be found at https://github.com/ziyangz5/CrystalNet

DSGI‐Net: Density‐based Selective Grouping Point Cloud Learning Network for Indoor Scene

AbstractIndoor scene point clouds exhibit diverse distributions and varying levels of sparsity, characterized by more intricate geometry and occlusion compared to outdoor scenes or individual objects. Despite recent advancements in 3D point cloud analysis introducing various network architectures, there remains a lack of frameworks tailored to the unique attributes of indoor scenarios. To address this, we propose DSGI‐Net, a novel indoor scene point cloud learning network that can be integrated into existing models. The key innovation of this work is selectively grouping more informative neighbor points in sparse regions and promoting semantic consistency of the local area where different instances are in proximity but belong to distinct categories. Furthermore, our method encodes both semantic and spatial relationships between points in local regions to reduce the loss of local geometric details. Extensive experiments on the ScanNetv2, SUN RGB‐D, and S3DIS indoor scene benchmarks demonstrate that our method is straightforward yet effective.

Spatially and Temporally Optimized Audio‐Driven Talking Face Generation

AbstractAudio‐driven talking face generation is essentially a cross‐modal mapping from audio to video frames. The main challenge lies in the intricate one‐to‐many mapping, which affects lip sync accuracy. And the loss of facial details during image reconstruction often results in visual artifacts in the generated video. To overcome these challenges, this paper proposes to enhance the quality of generated talking faces with a new spatio‐temporal consistency. Specifically, the temporal consistency is achieved through consecutive frames of the each phoneme, which form temporal modules that exhibit similar lip appearance changes. This allows for adaptive adjustment in the lip movement for accurate sync. The spatial consistency pertains to the uniform distribution of textures within local regions, which form spatial modules and regulate the texture distribution in the generator. This yields fine details in the reconstructed facial images. Extensive experiments show that our method can generate more natural talking faces than previous state‐of‐the‐art methods in both accurate lip sync and realistic facial details.

The effect of patients' socioeconomic status in rehabilitation centers on the efficiency and performance.

Patients' socioeconomic status on hospitals' efficiency in controlling for clinical component characteristics may have a role that has few been studied in rehabilitation centers. Because of the national health insurance system, rehabilitation centers are free of charge. To answer whether a patient's socioeconomic status (SES) is associated with efficiency and performance, we use a counterfactual analysis to get the patient's SES effect "as if" the patient's case was identical to whatever hospital. We restrained the data to patients from public acute care units where the decision on rehabilitation sector admission is based on availability, limiting bias by confounding factors. Besides, an analysis of six pathologies led to the same results. An exhaustive, detailed administrative database on rehabilitation center stays in France. To define the patients' socioeconomic status, we use two sources of data: the information collected at the time of the patient's entry into rehabilitation care and the information collected during the patient's stay in acute care. This double information avoids possible loss of socio-economic details between the two admissions. Patients recruited were exhaustively admitted over the year 2018 for stroke, chronic obstructive pulmonary disease, heart failure, or total hip replacement in France in the acute care unit and then in a rehab center. Mainly the elderly population. Information on patients' demography, comorbidities, and SES are coded due to the reimbursement system. Different dimensions controlling for factors (hospital ownership, patient clinical characteristics, rehabilitation care specificities, medical staff detailed information, and patients' socioeconomic status), were progressively added to control for any differences in baseline data between the two groups. We assess rehabilitation centers' efficiency by combining selected outcome quality indicators (Physical score improvement, Cognitive score improvement, Mortality, Return-to-home). The specific Providers' Activity Index is used to get the performance index. The performance of healthcare institutions is correlated not only to the case mix of their patients but also to the socioeconomic status of the patients admitted. The performance needs to be seen in light of patients' socioeconomic status. The data reveals that patients' socioeconomic status affects rehabilitation care efficiency and performance. In controlling patients' socioeconomic status, for-profit rehabilitation hospitals seemed more efficient than public ones.

Cascaded hybrid convolutional autoencoder network for spectral-spatial nonlinear hyperspectral unmixing

ABSTRACT Due to the complex interaction of photons between materials, linear hyperspectral unmixing (HU) is limited in real scenarios, making nonlinear unmixing a promising alternative. With the advancement of deep learning (DL), nonlinear unmixing methods based on the convolutional autoencoder (CAE) have gained considerable traction in HU. However, these unmixing methods struggle to integrate spectral and spatial information while reducing the loss of material details during the unmixing process. Therefore, we propose a cascaded hybrid CAE nonlinear unmixing network, called CHCANet, which effectively leverages convolutional combinations to deeply explore the spectral-spatial information from hyperspectral data and preserve the material details through self-perception. Specifically, each CAE in CHCANet combines 1-D and 2-D convolutions, fully utilizing the flexibility and simplicity of 1-D convolutions to capture spectral features and the spatial correlation handling capability of the 2-D convolution. Moreover, we apply the self-perception mechanism to the nonlinear HU task, which can establish the cycle consistency of the network, strengthen mutual connections between encoders, and effectively preserve high-level semantic information. Following this, the optimized self-perception loss further enhances CHCANet’s perception capability of nonlinear components and strengthens the connection between the decoder directly associated with image reconstruction. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of CHCANet and show excellent competitiveness compared to state-of-the-art unmixing methods.

Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs

The task of predicting entities and relations in Temporal Knowledge Graph (TKG) extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as Gated Recurrent Unit (GRU) models, primarily focus on encoding historical factual features within TKGs, often neglecting the importance of incorporating entities and relational features during decoding. This bias ultimately leads to loss of detail and inadequate prediction accuracy during the inference process. To address this issue, a novel ChronoBridge framework is proposed that features a dual mechanism of a chronological node encoder and a bridged feature fusion decoder. Specifically, the chronological node encoder employs an advanced recursive neural network with an enhanced GRU in an autoregressive manner to model historical KG sequences, thereby accurately capturing entity changes over time and significantly enhancing the model’s ability to identify and encode temporal patterns of facts across the timeline. Meanwhile, the bridged feature fusion decoder utilizes a new variant of GRU and a multilayer perception mechanism during the prediction phase to extract entity and relation features and fuse them for inference, thereby strengthening the reasoning capabilities of the model for future events. Testing on three standard datasets showed significant improvements, with a 25.21% increase in MRR accuracy and a 39.38% enhancement in relation inference. This advancement not only improves the understanding of temporal evolution in knowledge graphs but also sets a foundation for future research and applications of TKG reasoning.

Multi exposure fusion for high dynamic range imaging via multi-channel gradient tensor

Multi-exposure fusion (MEF) is an effective technique for directly fusing a sequence of low dynamic range (LDR) images from a high dynamic range (HDR) natural scene. The goal is to generate an information enriched LDR image. Despite its effectiveness, current MEF methods often encounter issues such as detail loss and color degradation. Additionally, existing algorithms often struggle to balance image quality and computation time, particularly for large-sized images. This paper introduces an innovative MEF algorithm that address these challenges, offering improved performance and computational time across all image sizes. The algorithm employs a multi-channel gradient tensor on RGB images to effectively capture the contrast information among the three channels. This mechanism allows an edge-preserving image filter to maintain edges while smoothing weight maps. To enhance computational efficiency, the algorithm uses a fast approximation method suitable for large sized images. Our comprehensive experimental results demonstrate that the proposed method outperforms existing MEF techniques both quantitatively and qualitatively. Furthermore, our method reduces computational time by approximately 30% compared to the most recent state-of-the-art techniques.

Research on Target Image Classification in Low-Light Night Vision.

In extremely dark conditions, low-light imaging may offer spectators a rich visual experience, which is important for both military and civic applications. However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen's features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets.

A Deep Learning-Based Two-Branch Generative Adversarial Network for Image De-Raining

Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two parts: a generative network and an adversarial network. The generative network performs image de-raining. The adversarial network determines whether the input image is rain-free or de-rained. The generative network comprises two branches: the A branch, which follows a traditional convolutional network structure, and the U branch, which utilizes a U-Net architecture. The A branch includes a multi-scale module for extracting information at different scales and a residual attention module to reduce redundant information interference. The U branch contains an encoder module designed to address the loss of details and local information caused by conventional down-sampling. To improve the performance of the generative network in image de-raining, this paper employs a relative discriminator incorporating a mean squared error loss. This discriminator better measures the differences between rainy and rain-free images while effectively preventing the occurrence of gradient vanishing. Finally, this study performs visual and quantitative comparisons of the proposed method and existing methods on three established rain image datasets. In the quantitative experiments, the proposed method outperforms existing methods regarding PSNR, SSIM, and VIF metrics. Specifically, our method achieves an average PSNR, SSIM, and VIF of approximately 5%, 3%, and 4% higher than the MFAA-GAN method, respectively. These results indicate that the de-rained images generated via the proposed method are closer to rain-free images.

UNeXt: An Efficient Network for the Semantic Segmentation of High-Resolution Remote Sensing Images

The application of deep neural networks for the semantic segmentation of remote sensing images is a significant research area within the field of the intelligent interpretation of remote sensing data. The semantic segmentation of remote sensing images holds great practical value in urban planning, disaster assessment, the estimation of carbon sinks, and other related fields. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is gradually increasing. This increase in resolution brings about challenges such as significant changes in the scale of ground objects, redundant information, and irregular shapes within remote sensing images. Current methods leverage Transformers to capture global long-range dependencies. However, the use of Transformers introduces higher computational complexity and is prone to losing local details. In this paper, we propose UNeXt (UNet+ConvNeXt+Transformer), a real-time semantic segmentation model tailored for high-resolution remote sensing images. To achieve efficient segmentation, UNeXt uses the lightweight ConvNeXt-T as the encoder and a lightweight decoder, Transnext, which combines a Transformer and CNN (Convolutional Neural Networks) to capture global information while avoiding the loss of local details. Furthermore, in order to more effectively utilize spatial and channel information, we propose a SCFB (SC Feature Fuse Block) to reduce computational complexity while enhancing the model’s recognition of complex scenes. A series of ablation experiments and comprehensive comparative experiments demonstrate that our method not only runs faster than state-of-the-art (SOTA) lightweight models but also achieves higher accuracy. Specifically, our proposed UNeXt achieves 85.2% and 82.9% mIoUs on the Vaihingen and Gaofen5 (GID5) datasets, respectively, while maintaining 97 fps for 512 × 512 inputs on a single NVIDIA GTX 4090 GPU, outperforming other SOTA methods.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

Effects of varied inundation characteristics on early life stages of a salt marsh plant

Tidal inundation is a major stress in salt marshes that regulates the patterns of plant distribution and the associated functions provided by vegetation communities. Usually, frequency is used to represent inundation intensity and can be estimated using elevation. However, frequency is only a statistical indicator of tidal inundation conditions during a given period, which ignores many details of tidal inundation characteristics based on a single tidal event. On the scale of a single tidal event, duration and water depth are important characteristics for describing inundation conditions, which vary along the elevation gradient. The frequency of tidal events of a specific duration and water depth also varied. To unravel the impact of varied inundation characteristics on the key life stages of a foundation plant, we designed an experiment with varied inundation treatments of different frequencies, durations, and depths. Our results showed that the frequency, duration, and depth of inundation events significantly influenced seed emergence, seedling survival, and growth. Stress can be strengthened by a higher frequency with a longer duration and larger depth. Among these factors, frequency had a dominant impact, followed by duration and water depth. Specifically, there is a trade-off between frequency, duration, and depth, suggesting that an inundation event with shallower depth and/or shorter duration would reduce the stress from higher frequency. The findings fill a gap in the loss of details of varied inundation characteristics on plant establishment on a fine scale. Further, it will help explicit inundation stress more accurately and clearly and provide important implications for stress relief solutions in coastal ecological restoration.

Fast reconstruction 3D computed tomography image of stacked cell under faster scanning by dual-branch cross-fusion flat bottom network

Abstract Cone beam computed tomography (CBCT) fast scanning and reconstruction is a key step to achieve rapid detection of internal defects in batteries. In this work, we have achieved a faster CT scanning just in 5 seconds by reducing the X-ray exposure time in sparse view CT. However, the CT data is extremely incomplete by faster scanning; the existing reconstruction methods are difficult to reconstruct a high quality three-dimensional (3D) CT image of stacked cells. To address this issue, we propose a 3D CT image reconstruction network, which can reconstruct higher quality CT images from low quality 3D volume data. The input data of the reconstruction network is not 2D projection data, but 3D volume data. In this network, a high and low resolution dual-branch cross-fusion flat bottom structure is designed. The high resolution flat bottom branch aims to preserve detailed information, while the low resolution flat bottom branch focuses on capturing more semantic information. Cross-fusion between these branches mitigates the loss of semantic details. Additionally, the auxiliary loss function, the main loss function, and the 3D attention module are designed to enhance semantic accuracy and the learning performance of the network. The 3D training data is collected under a fast scanning strategy spanning 5-60 seconds. During the training phase, we use clipping block technology to cut the 3D volume data, enabling direct training on the 3D volume data. Our experimental results demonstrate that our 3D reconstruction network outperforms mainstream algorithms under this faster scanning strategy, which is able to reconstruct higher quality 3D CT images just in 15 seconds. Ablation experiments confirm the positive impact of the dual-branch cross-fusion flat bottom structure, attention module, and loss functions on improving the quality of 3D CT images.

Dehazing network based on residual context attention and cross-layer fusion

Current image dehazing algorithms based on deep learning usually use traditional convolutional layers when extracting features, which easily causes the loss of image details and edge information, ignores the position information of the image when extracting features, and ignores the original information of the image when fusing features, and cannot restore high-quality haze-free images with complete structure and clarity. To address this problem, a dehazing algorithm based on residual context attention and cross-layer feature fusion is proposed. Firstly, the residual group structure is obtained by serializing the proposed residual context blocks, and the features of the first two layers of the network, i.e., the shallow layers, are extracted to obtain rich context information of the shallow layers; secondly, coordinate attention is introduced to establish an attention map with position information, and it is applied to the residual context feature extraction and placed in the third layer of the network, i.e., the deep layer, to extract deeper semantic information; then, in the middle layer of the network, feature information from different resolution streams is fused across layers to enhance the information exchange between the shallow and deep layers to achieve the purpose of feature enhancement; finally, the features with rich semantic information obtained by the network are aggregated with the original input, thereby improving the restoration effect. Experimental results on the RESIDE dataset and the Haze4K dataset show that the proposed algorithm achieves good results in both visual effects and objective indicators.

Detection of tea leaf blight in UAV remote sensing images by integrating super-resolution and detection networks.

Tea leaf blight (TLB) is a common disease of tea plants and is widely distributed in tea gardens. Although the use of unmanned aerial vehicle (UAV) remote sensing can help to achieve a wider scale for TLB detection, the blurring of UAV images, overlapping of tea leaves, and small size of TLB spots pose significant challenges to the task of detection. This study proposes a method of detecting TLB in UAV remote sensing images by integrating super-resolution (SR) and detection networks. We use an SR network called SERB-Swin2sr to reconstruct the detailed features of UAV images and solve the problem of detail loss caused by the blurring in UAV images. In SERB-Swin2sr, a squeeze-and-excitation ResNet block (SERB) is introduced to enhance the models' ability to extract the target details in the images, and the convolution stem replaces the convolution block in order to increase the convergence rate and stability of the network. A detection network called SDDA-YOLO is applied to achieve precise detection of TLB in UAV remote sensing images. In SDDA-YOLO, a shuffle dual-dimensional attention (SDDA) module is introduced to enhance the feature fusion capability of the network, and an Xsmall-scale detection layer is used to enhance the detection ability of small lesions. Experimental results show that the proposed method is superior to current detection methods. Compared with a baseline YOLOv8 model, the precision, mAP@0.5, and mAP@0.5:0.95 of the proposed method are improved by 4.2%, 1.6%, and 1.8%, and the size of our model is only 4.6MB.

DBSF-Net: Infrared Image Colorization Based on the Generative Adversarial Model with Dual-Branch Feature Extraction and Spatial-Frequency-Domain Discrimination

Thermal infrared cameras can image stably in complex scenes such as night, rain, snow, and dense fog. Still, humans are more sensitive to visual colors, so there is an urgent need to convert infrared images into color images in areas such as assisted driving. This paper studies a colorization method for infrared images based on a generative adversarial model. The proposed dual-branch feature extraction network ensures the stability of the content and structure of the generated visible light image; the proposed discrimination strategy combining spatial and frequency domain hybrid constraints effectively improves the problem of undersaturated coloring and the loss of texture details in the edge area of the generated visible light image. The comparative experiment of the public infrared visible light paired data set shows that the algorithm proposed in this paper has achieved the best performance in maintaining the consistency of the content structure of the generated image, restoring the image color distribution, and restoring the image texture details.

GeoHi-GNN: Geometry-aware hierarchical graph representation learning for normal estimation

Normal estimation has been one of the key tasks in point cloud analysis, while it is challenging when facing with severe noises or complex regions. The challenges mainly come from the selection of supporting points for estimation, that is, improper selections of points and points' scale will lead to insufficient information, loss of details, etc. To this end, this paper proposes one feature-centric fitting scheme, GeoHi-GNN, by learning geometry-aware hierarchical graph representation for fitting weights estimation. The main functional module is the continuously conducted Hierarchically Geometric-aware (HG) module, consisting of two core operations, namely, the graph node construction (GNC) and the geometric-aware dynamic graph convolution (GDGC). GNC aims to aggregate the feature information onto a smaller number of nodes, providing global-to-local information while avoiding the interferences from noises in larger scales. With these nodes distributed in different scales, GDGC dynamically updates the node features regarding to both intrinsic feature and extrinsic geometric information. Finally, the hierarchical graphical features are cascaded to estimate the weights for supporting points in the surface fitting. Through the extensive experiments and comprehensive comparisons with the state-of-the-arts, our scheme has exhibited many attractive advantages such as being geometry-aware and robust, empowering further applications like more accurate surface reconstruction.

Enhancing Underwater Images through Multi-Frequency Detail Optimization and Adaptive Color Correction

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.

WTCL-Dehaze: Rethinking Real-World Image Dehazing via Wavelet Transform and Contrastive Learning

Images captured in hazy outdoor conditions often suffer from colour distortion, low contrast, and loss of detail, which impair high-level vision tasks. Single image dehazing is essential for applications such as autonomous driving and surveillance, with the aim of restoring image clarity. In this work, we propose WTCL-Dehaze an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform (DWT). We incorporate contrastive regularization to enhance feature representation by contrasting hazy and clear image pairs. Additionally, we utilize DWT for multi-scale feature extraction, effectively capturing high-frequency details and global structures. Our approach leverages both labelled and unlabelled data to mitigate the domain gap and improve generalization. The model is trained on a combination of synthetic and real-world datasets, ensuring robust performance across different scenarios. Extensive experiments demonstrate that our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods on both benchmark datasets and real-world images.

Low-light image enhancement guided by multi-domain features for detail and texture enhancement

Low-light image enhancement holds significant value in the fields of computer vision and image processing, such as in applications like surveillance photography and medical imaging. Images captured in low-light environments typically suffer from significant noise levels, low contrast, and color distortion. Although existing low-light image enhancement techniques can improve image brightness and contrast to some extent, they often introduce noise or result in over-enhancement, leading to the loss of detail and texture. This paper introduces an innovative approach to low-light image enhancement by fusing spatial and frequency domain features while optimizing them with multiple loss functions. The core of the algorithm lies in its multi-branch feature extraction, multi-loss function constraints, and a carefully designed model structure. In particular, the model employs an encoder-decoder architecture, where the encoder extracts spatial features from the image, the Fourier feature extraction module captures frequency domain information, and the histogram feature encoder-decoder module processes global brightness distribution. These extracted features are then fused and reconstructed in the decoder to produce the enhanced image. In terms of loss functions, the algorithm combines perceptual loss, structural similarity loss, Fourier loss, and histogram loss to ensure comprehensive and natural enhancement effects. The novelty of this algorithm lies not only in its multi-branch feature extraction design but also in its unique model structure, which synergistically improves image quality across different domains, effectively preventing over-enhancement, and ultimately achieving a balanced enhancement of brightness, details, and texture. Experimental results on multiple datasets, including SIDD, LOL, MIT-Adobe-FiveK, and LOL-v2-synthetic, demonstrate that the proposed method outperforms existing techniques in terms of image detail, texture, and brightness. Specifically, it achieves a PSNR of 27.52 dB on the LOL dataset, surpassing Wavelet Diffusion by 1.19 dB. Additionally, on the LOL-v2-synthetic dataset, it achieves a PSNR of 29.56 dB, exceeding Wavelet Diffusion by 3.06 dB. These results demonstrate a significant enhancement in the visual quality of low-light images.