LL-GaussianMap: Zero-shot Low-Light Image Enhancement via 2D Gaussian Splatting Guided Gain Maps

Significant progress has been made in low-light image enhancement with respect to visual quality. However, most existing methods primarily operate in the pixel domain or rely on implicit feature representations. As a result, the intrinsic geometric structural priors of images are often neglected. 2D Gaussian Splatting (2DGS) has emerged as a prominent explicit scene representation technique characterized by superior structural fitting capabilities and high rendering efficiency. Despite these advantages, the utilization of 2DGS in low-level vision tasks remains unexplored. To bridge this gap, LL-GaussianMap is proposed as the first unsupervised framework incorporating 2DGS into low-light image enhancement. Distinct from conventional methodologies, the enhancement task is formulated as a gain map generation process guided by 2DGS primitives. The proposed method comprises two primary stages. First, high-fidelity structural reconstruction is executed utilizing 2DGS. Then, data-driven enhancement dictionary coefficients are rendered via the rasterization mechanism of Gaussian splatting through an innovative unified enhancement module. This design effectively incorporates the structural perception capabilities of 2DGS into gain map generation, thereby preserving edges and suppressing artifacts during enhancement. Additionally, the reliance on paired data is circumvented through unsupervised learning. Experimental results demonstrate that LL-GaussianMap achieves superior enhancement performance with an extremely low storage footprint, highlighting the effectiveness of explicit Gaussian representations for image enhancement.

GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution

Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture and Feature Modulation Network (GTFMN), a novel framework that decouples the LLSR task into two sub-problems: illumination estimation and texture restoration. First, our network employs a dedicated Illumination Stream whose purpose is to predict a spatially varying illumination map that accurately captures lighting distribution. Further, this map is utilized as an explicit guide within our novel Illumination Guided Modulation Block (IGM Block) to dynamically modulate features in the Texture Stream. This mechanism achieves spatially adaptive restoration, enabling the network to intensify enhancement in poorly lit regions while preserving details in well-exposed areas. Extensive experiments demonstrate that GTFMN achieves the best performance among competing methods on the OmniNormal5 and OmniNormal15 datasets, outperforming them in both quantitative metrics and visual quality.

Facial Emotion Recognition on FER-2013 using an EfficientNetB2-Based Approach

Detection of human emotions based on facial images in real-world scenarios is a difficult task due to low image quality, variations in lighting, pose changes, background distractions, small inter-class variations, noisy crowd-sourced labels, and severe class imbalance, as observed in the FER-2013 dataset of 48x48 grayscale images. Although recent approaches using large CNNs such as VGG and ResNet achieve reasonable accuracy, they are computationally expensive and memory-intensive, limiting their practicality for real-time applications. We address these challenges using a lightweight and efficient facial emotion recognition pipeline based on EfficientNetB2, trained using a two-stage warm-up and fine-tuning strategy. The model is enhanced with AdamW optimization, decoupled weight decay, label smoothing (epsilon = 0.06) to reduce annotation noise, and clipped class weights to mitigate class imbalance, along with dropout, mixed-precision training, and extensive real-time data augmentation. The model is trained using a stratified 87.5%/12.5% train-validation split while keeping the official test set intact, achieving a test accuracy of 68.78% with nearly ten times fewer parameters than VGG16-based baselines. Experimental results, including per-class metrics and learning dynamics, demonstrate stable training and strong generalization, making the proposed approach suitable for real-time and edge-based applications.

LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment

Due to silence in early stages, lung cancer has been one of the most leading causes of mortality in cancer patients world-wide. Moreover, major symptoms of lung cancer are hard to differentiate with other respiratory disease symptoms such as COPD, further leading patients to overlook cancer progression in early stages. Thus, to enhance survival rates in lung cancer, early detection from consistent proactive respiratory system monitoring becomes crucial. One of the most prevalent and effective methods for lung cancer monitoring would be low-dose computed tomography(LDCT) chest scans, which led to remarkable enhancements in lung cancer detection or tumor classification tasks under rapid advancements and applications of computer vision based AI models such as EfficientNet or ResNet in image processing. However, though advanced CNN models under transfer learning or ViT based models led to high performing lung cancer detections, due to its intrinsic limitations in terms of correlation dependence and low interpretability due to complexity, expansions of deep learning models to lung cancer treatment analysis or causal intervention analysis simulations are still limited. Therefore, this research introduced LungCRCT: a latent causal representation learning based lung cancer analysis framework that retrieves causal representations of factors within the physical causal mechanism of lung cancer progression. With the use of advanced graph autoencoder based causal discovery algorithms with distance Correlation disentanglement and entropy-based image reconstruction refinement, LungCRCT not only enables causal intervention analysis for lung cancer treatments, but also leads to robust, yet extremely light downstream models in malignant tumor classification tasks with an AUC score of 93.91%.

Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective

In the current era of mobile internet, Lightweight Low-Light Image Enhancement (L3IE) is critical for mobile devices, which faces a persistent trade-off between visual quality and model compactness. While recent methods employ disentangling strategies to simplify lightweight architectural design, such as Retinex theory and YUV color space transformations, their performance is fundamentally limited by overlooking channel-specific degradation patterns and cross-channel interactions. To address this gap, we perform a frequency-domain analysis that confirms the superiority of the YUV color space for L3IE. We identify a key insight: the Y channel primarily loses low-frequency content, while the UV channels are corrupted by high-frequency noise. Leveraging this finding, we propose a novel YUV-based paradigm that strategically restores channels using a Dual-Stream Global-Local Attention module for the Y channel, a Y-guided Local-Aware Frequency Attention module for the UV channels, and a Guided Interaction module for final feature fusion. Extensive experiments validate that our model establishes a new state-of-the-art on multiple benchmarks, delivering superior visual quality with a significantly lower parameter count.

LL-GaussianImage: Efficient Image Representation for Zero-shot Low-Light Enhancement with 2D Gaussian Splatting

2D Gaussian Splatting (2DGS) is an emerging explicit scene representation method with significant potential for image compression due to high fidelity and high compression ratios. However, existing low-light enhancement algorithms operate predominantly within the pixel domain. Processing 2DGS-compressed images necessitates a cumbersome decompression-enhancement-recompression pipeline, which compromises efficiency and introduces secondary degradation. To address these limitations, we propose LL-GaussianImage, the first zero-shot unsupervised framework designed for low-light enhancement directly within the 2DGS compressed representation domain. Three primary advantages are offered by this framework. First, a semantic-guided Mixture-of-Experts enhancement framework is designed. Dynamic adaptive transformations are applied to the sparse attribute space of 2DGS using rendered images as guidance to enable compression-as-enhancement without full decompression to a pixel grid. Second, a multi-objective collaborative loss function system is established to strictly constrain smoothness and fidelity during enhancement, suppressing artifacts while improving visual quality. Third, a two-stage optimization process is utilized to achieve reconstruction-as-enhancement. The accuracy of the base representation is ensured through single-scale reconstruction and network robustness is enhanced. High-quality enhancement of low-light images is achieved while high compression ratios are maintained. The feasibility and superiority of the paradigm for direct processing within the compressed representation domain are validated through experimental results.

Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography

X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.

RetinexGuI: Retinex-Guided Iterative Illumination Estimation Method for Low Light Images

In recent years, there has been a growing interest in low-light image enhancement (LLIE) due to its importance for critical downstream tasks. Current Retinex-based methods and learning-based approaches have shown significant LLIE performance. However, computational complexity and dependencies on large training datasets often limit their applicability in real-time applications. We introduce RetinexGuI, a novel and effective Retinex-guided LLIE framework to overcome these limitations. The proposed method first separates the input image into illumination and reflection layers, and iteratively refines the illumination while keeping the reflectance component unchanged. With its simplified formulation and computational complexity of $\mathcal{O}(N)$, our RetinexGuI demonstrates impressive enhancement performance across three public datasets, indicating strong potential for large-scale applications. Furthermore, it opens promising directions for theoretical analysis and integration with deep learning approaches. The source code will be made publicly available at https://github.com/etuspars/RetinexGuI once the paper is accepted.

Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events

Novel view synthesis from low dynamic range (LDR) blurry images, which are common in the wild, struggles to recover high dynamic range (HDR) and sharp 3D representations in extreme lighting conditions. Although existing methods employ event data to address this issue, they ignore the sensor-physics mismatches between the camera output and physical world radiance, resulting in suboptimal HDR and deblurring results. To cope with this problem, we propose a unified sensor-physics grounded NeRF framework for sharp HDR novel view synthesis from single-exposure blurry LDR images and corresponding events. We employ NeRF to directly represent the actual radiance of the 3D scene in the HDR domain and model raw HDR scene rays hitting the sensor pixels as in the physical world. A pixel-wise RGB mapping field is introduced to align the above rendered pixel values with the sensor-recorded LDR pixel values of the input images. A novel event mapping field is also designed to bridge the physical scene dynamics and actual event sensor output. The two mapping fields are jointly optimized with the NeRF network, leveraging the spatial and temporal dynamic information in events to enhance the sharp HDR 3D representation learning. Experiments on the collected and public datasets demonstrate that our method can achieve state-of-the-art deblurring HDR novel view synthesis results with single-exposure blurry LDR images and corresponding events.

SHARE: A Fully Unsupervised Framework for Single Hyperspectral Image Restoration

Hyperspectral image (HSI) restoration is a fundamental challenge in computational imaging and computer vision. It involves ill-posed inverse problems, such as inpainting and super-resolution. Although deep learning methods have transformed the field through data-driven learning, their effectiveness hinges on access to meticulously curated ground-truth datasets. This fundamentally restricts their applicability in real-world scenarios where such data is unavailable. This paper presents SHARE (Single Hyperspectral Image Restoration with Equivariance), a fully unsupervised framework that unifies geometric equivariance principles with low-rank spectral modelling to eliminate the need for ground truth. SHARE's core concept is to exploit the intrinsic invariance of hyperspectral structures under differentiable geometric transformations (e.g. rotations and scaling) to derive self-supervision signals through equivariance consistency constraints. Our novel Dynamic Adaptive Spectral Attention (DASA) module further enhances this paradigm shift by explicitly encoding the global low-rank property of HSI and adaptively refining local spectral-spatial correlations through learnable attention mechanisms. Extensive experiments on HSI inpainting and super-resolution tasks demonstrate the effectiveness of SHARE. Our method outperforms many state-of-the-art unsupervised approaches and achieves performance comparable to that of supervised methods. We hope that our approach will shed new light on HSI restoration and broader scientific imaging scenarios. The code will be released at https://github.com/xuwayyy/SHARE.