Under-water-image-enhancement
Underwater images suffer from color distortion, haze, scattering, and uneven lighting. This paper proposes a MATLAB-based restoration method using constrained color compensation, dark channel prior, guided filtering, MSRCR, and adaptive histogram equalization. It improves color, contrast, brightness, and structural details.
Details
- External ID
- 1362168950
- Source
- GITHUB
- Company
- —
- Product
- Under-water-image-enhancement
- Website domain
- github.com
- Launched
- Sept. 9, 2026
- Cohort
- —
- Upvotes
- 12
- Upvotes percentile
- 0.3866256725595696
- Tags
- —
- Fetched at
- Sept. 13, 2026, 5:57 p.m.
- Updated at
- Sept. 13, 2026, 5:57 p.m.
Enrichment
- Theme
- niche creative and graphics software
- Vertical
- Media & entertainment
- Function
- Dev tools
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Hobby / open-source project
- Normalized one-liner
- matlab image restoration for underwater photography
- Manually corrected
- False
Could you build this?
Partial The classical computer vision algorithms (dark channel prior, guided filtering, MSRCR, CLAHE) are well-documented in academic papers and can be re-implemented, but tuning them into a robust scientific image pipeline requires domain expertise in signal processing.
What it would actually take: Requires implementing image restoration algorithms using MATLAB or Python (NumPy, OpenCV, SciPy). Hard parts involve optimizing guided filtering to avoid halo artifacts, calibrating the attenuation coefficients for underwater wavelength scattering, and tuning multi-scale retinex with color restoration (MSRCR) parameters across varied water turbidity conditions.
Competitors
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Attention rank: #104 of 201 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 310 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.
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