DST-Net: A Dual-Stream Transformer with Illumination-Independent Feature Guidance and Multi-Scale Spatial Convolution for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2603.16482v1
• Date: Tue, 17 Mar 2026 13:07:32 GMT
• Title: DST-Net: A Dual-Stream Transformer with Illumination-Independent Feature Guidance and Multi-Scale Spatial Convolution for Low-Light Image Enhancement
• Abstract summary: Low-light image enhancement aims to restore the visibility of images captured by visual sensors in dim environments.<n>We propose a Dual-Stream Transformer Network (DST-Net) based on illumination-agnostic signal prior guidance and multi-scale spatial convolutions.<n>Our method achieves a PSNR of 25.64 dB on the LOL dataset.
• Score: 9.193933558950341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement aims to restore the visibility of images captured by visual sensors in dim environments by addressing their inherent signal degradations, such as luminance attenuation and structural corruption. Although numerous algorithms attempt to improve image quality, existing methods often cause a severe loss of intrinsic signal priors. To overcome these challenges, we propose a Dual-Stream Transformer Network (DST-Net) based on illumination-agnostic signal prior guidance and multi-scale spatial convolutions. First, to address the loss of critical signal features under low-light conditions, we design a feature extraction module. This module integrates Difference of Gaussians (DoG), LAB color space transformations, and VGG-16 for texture extraction, utilizing decoupled illumination-agnostic features as signal priors to continuously guide the enhancement process. Second, we construct a dual-stream interaction architecture. By employing a cross-modal attention mechanism, the network leverages the extracted priors to dynamically rectify the deteriorated signal representation of the enhanced image, ultimately achieving iterative enhancement through differentiable curve estimation. Furthermore, to overcome the inability of existing methods to preserve fine structures and textures, we propose a Multi-Scale Spatial Fusion Block (MSFB) featuring pseudo-3D and 3D gradient operator convolutions. This module integrates explicit gradient operators to recover high-frequency edges while capturing inter-channel spatial correlations via multi-scale spatial convolutions. Extensive evaluations and ablation studies demonstrate that DST-Net achieves superior performance in subjective visual quality and objective metrics. Specifically, our method achieves a PSNR of 25.64 dB on the LOL dataset. Subsequent validation on the LSRW dataset further confirms its robust cross-scene generalization.

Related papers

• ScaLe-INR: Scale and Learn Implicit Neural Representations [0.0]
  Implicit Neural Representations (INRs) parameterized by multilayer perceptrons excel at modeling continuous signals.<n>High-frequency weight updates destructively interfere with the underlying low-frequency structural approximation.<n>ScaLe-INR explicitly matches the signal's frequency spectrum with the optimal operating region of the INR.
  arXiv  Detail & Related papers  (2026-06-26T09:02:48Z)
• FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation [48.928051741750124]
  We propose a scalable image-to-3DGS framework that boosts representation learning and cross-modal alignment during generation.<n>FLUX3D yields substantial improvements in appearance fidelity and significantly outperforms all state-of-the-art (SOTA) methods in generating high-quality 3DGS assets.
  arXiv  Detail & Related papers  (2026-06-23T17:52:21Z)
• Implicit Neural Representations: A Signal Processing Perspective [54.57279006229212]
  Implicit neural representations (INRs) mark a fundamental shift in signal modeling, moving from discrete sampled data to continuous functional representations.<n>This article examines the evolution of INRs from a signal processing perspective, emphasizing spectral behavior, sampling theory, and multiscale representation.<n>We highlight the utility of INRs across a broad range of applications, including inverse problems in medical and radar imaging, compression, and 3D scene representation.
  arXiv  Detail & Related papers  (2026-04-16T14:12:06Z)
• One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion [57.824020826432815]
  We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images.<n>We design a Dual-Domain Detail Perception Module, which enables handling high-resolution images without being limited by the ViT backbone.<n>We develop a feature-guided diffusion network, which can preserve high-frequency details during the restoration process.
  arXiv  Detail & Related papers  (2026-01-20T17:11:55Z)
• MSF: Efficient Diffusion Model Via Multi-Scale Latent Factorize [18.73205699076486]
  We introduce a diffusion framework leveraging multi-scale latent factorization.<n>Our framework decomposes the denoising target, typically latent features from a pretrained Variational Autoencoder, into a low-frequency base signal.<n>Our proposed architecture facilitates reduced sampling steps during the residual learning stage.
  arXiv  Detail & Related papers  (2025-01-23T03:18:23Z)
• Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning [20.697932997351813]
  gradient-guided multi-frame super-resolution (MFSR) framework for remote sensing imagery reconstruction.<n>We propose a novel gradient-guided multi-frame super-resolution (MFSR) framework for remote sensing imagery reconstruction.
  arXiv  Detail & Related papers  (2024-12-13T04:19:48Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model [59.08821399652483]
  Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. We propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Reti-Diff comprises two key components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RG
  arXiv  Detail & Related papers  (2023-11-20T09:55:06Z)
• S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction [59.39343894089959]
  A snapshot compressive imager (CASSI) with Transformer reconstruction backend remarks high-fidelity sensing performance.<n> dominant spatial and spectral attention designs show limitations in hyperspectral modeling.<n>We propose a spatial-spectral (S2-) Transformer implemented by a paralleled attention design and a mask-aware learning strategy.
  arXiv  Detail & Related papers  (2022-09-24T19:26:46Z)
• D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion [22.0246327137227]
  This paper presents the fusion of the compressive measurements of a low-spatial high-spectral resolution coded aperture snapshot spectral imager (CASSI) architecture and a high-spatial low-spectral resolution multispectral color filter array (MCFA) system. Unlike previous CSIF works, this paper proposes joint optimization of the sensing architectures and a reconstruction network in an end-to-end (E2E) manner.
  arXiv  Detail & Related papers  (2022-05-24T15:39:34Z)
• SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification [12.566284647658053]
  We propose a novel spectrum-aware feature augementation network named SFANet for cross-modality matching problem. Learning with grayscale-spectrum images, our model can apparently reduce modality discrepancy and detect inner structure relations. In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks.
  arXiv  Detail & Related papers  (2021-02-24T08:57:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.