// Auto-enhance typically adjusts:
1. BRIGHTNESS
Dark photos → lift shadows
Overexposed → pull highlights
Target: mean brightness ~128
2. CONTRAST
Flat/hazy → increase range
Already punchy → leave alone
Target: full histogram spread
3. SATURATION
Washed out → boost colors
Oversaturated → pull back
Target: natural-looking vibrancy
4. SHARPNESS
Soft/blurry → apply unsharp mask
Already crisp → minimal touch
Target: edges defined, no halos
// MVP scope (3 sessions):
// ✅ Brightness, contrast, saturation
// ✅ Statistical analysis (histograms)
// ✅ Before/after comparison
// ✅ User-adjustable result
// ❌ ML-based (future iteration)
// ❌ Noise reduction (complex)
// ❌ Face detection (scope creep)
// User flow:
1. User uploads / has an image open
2. Clicks "✨ Auto-Enhance" in toolbar
3. Button shows loading spinner (1-2s)
4. Image updates with enhancements
5. Before/after slider appears
(drag to compare original vs result)
6. "Adjust Result" panel shows:
- Brightness: +15 (slider)
- Contrast: +20 (slider)
- Saturation: +10 (slider)
- User can tweak any value
7. "Apply" commits the change
"Revert" returns to original
// Key UX decisions:
// - Non-destructive: original preserved
// - Adjustable: user isn't stuck
// - Fast: Web Worker for analysis
// - Accessible: screen reader
// announces "Enhancement applied:
// brightness +15, contrast +20"
// Option A: Server-side analysis
// POST /api/enhance
// Body: { imageData: base64 }
// Response: { suggestions: {...} }
// Pro: can use ML later
// Con: upload latency, server cost
// Option B: Client-side analysis ✅
// Analyze in Web Worker (no upload)
// Pro: instant, no server cost
// Con: limited to JS math
// (Perfect for our MVP)
// Decision: Client-side for MVP.
// API endpoint for analytics only:
// POST /api/enhance/analytics
// Body: {
// imageId: string,
// analysis: ImageAnalysis,
// userAdjustments: Adjustments,
// applied: boolean,
// }
// Purpose: track usage patterns
// to improve algorithm later.
// Future: POST /api/enhance/ml
// Server-side ML analysis
// Behind feature flag
// Rolled out to premium users first
// Enhancement history
interface EnhancementRecord {
id: string;
imageId: string;
userId: string;
timestamp: Date;
// What the algorithm suggested
analysis: {
brightness: {
mean: number;
suggested: number;
};
contrast: {
range: number;
suggested: number;
};
saturation: {
mean: number;
suggested: number;
};
};
// What the user actually applied
// (null if they reverted)
applied: {
brightness: number;
contrast: number;
saturation: number;
} | null;
// Did the user adjust our
// suggestions?
userAdjusted: boolean;
}
// This data lets us measure:
// - How often is auto-enhance used?
// - How often do users adjust results?
// - What direction do they adjust?
// (algorithm too aggressive? timid?)
// - Data-driven algorithm improvement.
# Technical Design: Auto-Enhance
# Author: [You] Date: 2026-02-XX
## Problem
Users manually adjust brightness,
contrast, saturation. 73% of users
apply the same basic corrections.
One-click enhancement would save
time and improve results.
## Approach
Client-side image analysis using
histogram statistics. Suggest optimal
corrections. User can adjust or revert.
## Scope (3 sessions)
- 120: Backend analysis engine + tests
- 121: Frontend integration + UX
- 122: Edge cases, a11y, polish
## Non-Goals (this iteration)
- ML-based enhancement
- Noise reduction
- Face-specific corrections
## API
- Analysis: client-side Web Worker
- Analytics: POST /api/enhance/analytics
## Data Model
EnhancementRecord (see above)
## Risks
- "Already good" images get worse
→ Detect and skip minimal changes
- Very dark/light images over-correct
→ Clamp suggestions to safe ranges
- User expectation exceeds algorithm
→ Set clear "auto" vs "AI" framing
## Success Metrics
- 40%+ of users try auto-enhance
- <30% revert (70%+ keep result)
- <50% manually adjust (algorithm
is good enough for most)
Present your design document to the team (instructor/peers). Defend decisions. Incorporate feedback.
// Anticipate these questions:
"Why not use a pretrained ML model?"
→ Scope. MVP ships in 3 sessions.
ML is the future iteration.
Client-side math ships NOW.
"What about already-perfect images?"
→ Detect: if all suggestions < 5%,
show "Image looks great already!"
"How do you handle B&W photos?"
→ Skip saturation adjustment.
Detect via saturation variance.
"What if users hate the result?"
→ Non-destructive. One-click revert.
Adjustable sliders. Data collection
for algorithm improvement.
"What's the performance impact?"
→ Web Worker. ~200ms for 4K image.
Main thread never blocks.
// Incorporate feedback. Update the
// design doc. This is the process.
Design docs are how senior engineers scale their impact.
Code solves problems for users. Design docs solve problems for the team. Alignment before implementation prevents costly rewrites.