ArticleScientific reports2024
Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Underwater image enhancement using colour balancing and morphological residual processing through gamma correction.Scientific reports · 2026Article
- Detail-preserving denoising of CT and MRI images via adaptive clustering and non-local means algorithm.Scientific reports · 2025Article
- An image processing technique for optimizing industrial defect detection using dehazing algorithms.PloS one · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative
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Registered trials
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