BFORE: Butterfly-Firefly Optimized Retinex Enhancement for Low-Light Image Quality Improvement

• URL: http://arxiv.org/abs/2605.03509v2
• Date: Sat, 09 May 2026 09:52:29 GMT
• Title: BFORE: Butterfly-Firefly Optimized Retinex Enhancement for Low-Light Image Quality Improvement
• Abstract summary: Existing Retinex-based methods rely on manually tuned parameters that fail to generalize across diverse lighting conditions.<n>This paper proposes BFORE, a novel hybrid metaheuristic-optimized framework that automatically tunes the parameters of a Retinex-based pipeline.<n> BFORE produces the most naturally balanced mean brightness (129.97), closest to the ideal mid-tone value.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement is a fundamental challenge in computer vision and multimedia applications, as images captured under insufficient illumination suffer from poor visibility, low contrast, and color distortion. Existing Retinex-based methods rely on manually tuned parameters that fail to generalize across diverse lighting conditions. This paper proposes BFORE (Butterfly-Firefly Optimized Retinex Enhancement), a novel hybrid metaheuristic-optimized framework that automatically tunes the parameters of a multi-stage Retinex-based pipeline. The proposed method converts the input image to HSV color space and applies Adaptive Gamma Correction with Weighted Distribution (AGCWD) to the luminance channel, followed by adaptive denoising. A Butterfly Optimization Algorithm (BOA) optimizes the Multi-Scale Retinex with Color Restoration (MSRCR) parameters, while a Firefly Algorithm (FA) optimizes the AGCWD and denoising parameters. A hybrid BOA-FA switching strategy dynamically balances global exploration and local exploitation. Experimental evaluation on the LOL benchmark dataset (15 paired test images) demonstrates that BFORE achieves the highest PSNR (17.22 dB) among all traditional enhancement methods, with 20.3% improvement over Histogram Equalization and 17.5% over MSRCR. BFORE produces the most naturally balanced mean brightness (129.97), closest to the ideal mid-tone value. Notably, BFORE outperforms RetinexNet -- a deep learning baseline -- in both PSNR (17.22 vs. 16.77 dB) and SSIM (0.5417 vs. 0.4252) without requiring any training data. The hybrid BOA-FA optimization contributes a 12.3% PSNR improvement and 14.8% SSIM improvement over the unoptimized pipeline.

Related papers

• Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis [0.0]
  Clinical utility of fundus images is routinely compromised by non-uniform illumination, motion blur, and low contrast.<n>This study proposes a two-stage image enhancement pipeline combining luminosity correction via HSV colour space decomposition with Contrast Limited Adaptive Histogram Equalization.
  arXiv  Detail & Related papers  (2026-07-20T08:39:13Z)
• AIGS-Net: Compact Illumination Field Modeling via 2D Gaussian Splatting for Fast Low-Light Image Enhancement [19.712825613823778]
  This paper proposes an Adaptive Illumination Gaussian Splatting Network (AIGS-Net) for fast low-light enhancement.<n>Unlike conventional static priors, AIGS-Net constructs an input-adaptive 2D Gaussian Splatting illumination field.<n>Experiments show that AIGS-Net improves detail recovery and color fidelity while requiring only approximately 40 learnable parameters.
  arXiv  Detail & Related papers  (2026-06-16T14:45:55Z)
• Convex Optimization for Alignment and Preference Learning on a Single GPU [52.997197698288936]
  Fine-tuning large language models to align with human preferences has driven the success of systems such as Gemini and ChatGPT.<n>Direct Preference Optimization (DPO) offers a simpler alternative but has limitations such as inconsistent ranking accuracy and high dependence on GPU resources.<n>We propose the Convex Optimization for Alignment and Preference Learning Algorithm (COALA), a novel lightweight strategy with strong theoretical guarantees.
  arXiv  Detail & Related papers  (2026-05-22T05:25:00Z)
• FLARE-BO: Fused Luminance and Adaptive Retinex Enhancement via Bayesian Optimisation for Low-Light Robotic Vision [3.490087692799367]
  This paper proposes FLARE-BO, an extended framework that jointly optimises eight parameters spanning across gamma correction, LIME-style illumination normalisation, chrominance denoising, bilateral filtering, NLM denoising, Grey-World automatic white balance, and adaptive post smoothing.<n>Results show marked improvements of the proposed method over existing methods that were not specifically trained using this dataset.
  arXiv  Detail & Related papers  (2026-04-23T21:51:22Z)
• GSTurb: Gaussian Splatting for Atmospheric Turbulence Mitigation [4.479376052925207]
  Atmospheric turbulence causes significant image degradation due to pixel displacement (tilt) and blur, particularly in long-range imaging applications.<n>We propose a novel framework for atmospheric turbulence mitigation, GSTurb, which integrates optical flow-guided tilt correction and Gaussian splatting for modeling non-isoplanatic blur.
  arXiv  Detail & Related papers  (2026-02-26T09:37:27Z)
• Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images [5.266753902938501]
  We propose a Genetic Algorithm (GA) based parameter optimization framework for UAV applications.<n>Our contributions include: (1) a novel GA-based parameter optimization framework that eliminates manual tuning; (2) a comprehensive evaluation methodology using multiple image quality metrics; and (3) a practical solution for resource-constrained UAV systems.<n> Experimental results demonstrate that our GA-optimized approach reduces Mean Squared Error by 42.86% while increasing Peak Signal-to-Noise Ratio and Structural Similarity by 8.47% and 28.52%, respectively, compared with baseline configurations.
  arXiv  Detail & Related papers  (2025-12-05T04:00:18Z)
• Adaptive Parameter Optimization for Robust Remote Photoplethysmography [17.439033592683835]
  Projection-based Signal Mixing (PRISM) achieves state-of-the-art performance among unsupervised methods.<n>This validates that adaptive time series optimization significantly improves r across diverse conditions.
  arXiv  Detail & Related papers  (2025-11-26T20:45:09Z)
• Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark [58.61079960074608]
  Existing infrared image enhancement methods focus on tackling individual degradations.<n>All-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness.
  arXiv  Detail & Related papers  (2025-10-10T12:55:54Z)
• Optimized Pap Smear Image Enhancement: Hybrid PMD Filter-CLAHE Using Spider Monkey Optimization [0.0]
  This study introduces an optimized hybrid approach to enhance Pap smear image quality.<n>The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE)<n>The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson's contrast (MC) of 0.995, and entropy of 6.80.
  arXiv  Detail & Related papers  (2025-02-21T02:34:57Z)
• Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration [59.744840744491945]
  In this paper, we reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency.<n>To mitigate the considerable computational burden associated with iterative sampling, we propose cost-aware trajectory distillation.<n>We fine-tune a foundational diffusion model (FLUX) with 12B parameters by using our algorithms, producing a unified framework for handling 7 kinds of image restoration tasks.
  arXiv  Detail & Related papers  (2024-10-07T07:46:08Z)
• Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement [61.22119364400268]
  We propose a novel low-frequency consistency method, facilitating improved frequency disentanglement optimization. Noteworthy improvements are showcased across five popular benchmarks, with up to 7.68dB gains on PSNR achieved for six state-of-the-art models. Our approach maintains efficiency with only 88K extra parameters, setting a new standard in the challenging realm of low-light image enhancement.
  arXiv  Detail & Related papers  (2024-09-03T06:19:03Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)

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.