Contrast Enhancement¶
Class: ContrastEnhancementBlock
Source: inference.core.workflows.core_steps.classical_cv.contrast_enhancement.v1.ContrastEnhancementBlock
Enhance image contrast using histogram normalization (the algorithm from GIMP's Auto Levels). This block stretches the image histogram to use the full available range [0-255], improving visibility of features with low contrast.
How This Block Works¶
- Channel Analysis: For grayscale images, find min/max directly. For color images, analyze each channel independently.
- Histogram Normalization: For each channel, stretch values from [min, max] to [0, 255] using linear scaling:
output = (input - min) / (max - min) * 255 - Clipping: Values outside [0, 255] are clipped to the valid range
Common Use Cases¶
- Low-contrast medical imaging: Normalize tissue visibility across varying acquisition parameters
- Industrial inspection: Enhance subtle surface defects on dull materials
- Surveillance footage: Improve nighttime or backlit scene visibility
- Document scanning: Brighten poorly lit document photos
- Microscopy: Boost signal from weak fluorescence or phase-contrast images
Input Parameters¶
image : Input image to enhance (color or grayscale) - Can be single-channel, 3-channel (BGR), or 4-channel (BGRA) - Each channel is normalized independently for color images
clip_limit : Percentage of histogram range to skip at extremes (default: 0) - Range: 0-50 - 0: No clipping, entire histogram from min to max is used - 1-3: Skip dark and bright outliers (robust to noise) - 5-10: Very aggressive outlier removal - 20-50: Extreme outlier removal, may lose subtle details
contrast_multiplier : Multiplier for contrast scaling after normalization (default: 1.0) - Range: 0.1-5.0 - 1.0: No additional scaling, just normalization - 0.5-0.9: Reduce contrast for smoother images - 1.1-2.0: Increase contrast for more dramatic enhancement
normalize_brightness : Apply brightness normalization using midtone equalization (default: False) - False: Only histogram normalization and contrast scaling - True: After histogram normalization, apply midtone adjustment for balanced brightness
Outputs¶
image : Enhanced image with normalized contrast, same shape and type as input
Notes¶
- Sensitive to outliers: Extreme min/max values (single dark/bright pixels) stretch most of the histogram into a narrow range. Use morphological opening (Morphological Transformation v2) as preprocessing to remove spurious dark/bright specks.
- Color shift: For color images, each channel stretches independently, which can shift hue if channels have very different dynamic ranges
- Efficiency: Very fast — linear scan for min/max, then linear transformation per pixel
- Brightness normalization: When enabled, applies midtone stretch (gamma ≈ 1.3) for more balanced perceived brightness
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/contrast_enhancement@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
clip_limit |
int |
Percentage of histogram range to skip at dark and bright extremes. 0: use full range from min to max. 1-3: skip outliers (robust). 5-10: very aggressive outlier removal.. | ❌ |
contrast_multiplier |
float |
Multiplier for contrast scaling after normalization. 1.0: no additional scaling (just histogram normalization). <1.0: reduce contrast. >1.0: increase contrast for more dramatic enhancement.. | ❌ |
normalize_brightness |
bool |
Apply brightness normalization using midtone equalization. When False, only histogram normalization and contrast scaling are applied. When True, applies midtone adjustment for more balanced perceived brightness.. | ❌ |
The Refs column marks possibility to parametrise the property with dynamic values available
in workflow runtime. See Bindings for more info.
Available Connections¶
Compatible Blocks
Check what blocks you can connect to Contrast Enhancement in version v1.
- inputs:
Heatmap Visualization,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Stability AI Image Generation,Image Threshold,Line Counter Visualization,Trace Visualization,Blur Visualization,Camera Calibration,Depth Estimation,QR Code Generator,Stability AI Outpainting,Icon Visualization,SIFT Comparison,Morphological Transformation,Background Subtraction,Keypoint Visualization,Color Visualization,Perspective Correction,Bounding Box Visualization,Corner Visualization,Stitch Images,Halo Visualization,Image Blur,Image Convert Grayscale,Morphological Transformation,Camera Focus,Contrast Equalization,Halo Visualization,Stability AI Inpainting,Roboflow Visual Search,Classification Label Visualization,Triangle Visualization,Grid Visualization,Background Color Visualization,Mask Visualization,Ellipse Visualization,Pixelate Visualization,Reference Path Visualization,SIFT,Label Visualization,Image Slicer,Text Display,Dot Visualization,Polygon Visualization,Crop Visualization,Dynamic Crop,Absolute Static Crop,Circle Visualization,Image Contours,Image Preprocessing,Polygon Visualization,Relative Static Crop,Camera Focus,Model Comparison Visualization - outputs:
VLM As Classifier,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Qwen2.5-VL,Image Stack,Anthropic Claude,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,SmolVLM2,LMM For Classification,Single-Label Classification Model,Perspective Correction,Corner Visualization,Clip Comparison,Halo Visualization,Qwen-VL,Keypoint Detection Model,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Relative Static Crop,OpenRouter,OpenAI,Florence-2 Model,VLM As Detector,OpenAI,Motion Detection,Heatmap Visualization,OCR Model,Perception Encoder Embedding Model,Blur Visualization,Barcode Detection,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,YOLO-World Model,Google Gemini,Clip Comparison,Google Gemini,Background Subtraction,Keypoint Visualization,Buffer,Stitch Images,Florence-2 Model,Contrast Equalization,Mask Edge Snap,OpenAI,Qwen3-VL,Moondream2,VLM As Detector,Google Gemini,Triangle Visualization,CLIP Embedding Model,Detections Stabilizer,SIFT,Multi-Label Classification Model,Image Contours,Keypoint Detection Model,VLM As Classifier,Pixel Color Count,GLM-OCR,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Google Gemma API,Time in Zone,Semantic Segmentation Model,Image Threshold,Line Counter Visualization,Semantic Segmentation Model,Multi-Label Classification Model,Camera Calibration,ByteTrack Tracker,Google Vision OCR,Image Blur,Morphological Transformation,Camera Focus,Roboflow Vision Events,Stability AI Inpainting,Classification Label Visualization,SAM2 Video Tracker,Event Writer,Qwen3.5-VL,Mask Visualization,Llama 3.2 Vision,Dominant Color,Reference Path Visualization,Image Slicer,Label Visualization,Byte Tracker,Dot Visualization,Dynamic Crop,Detections Stitch,Circle Visualization,Llama 3.2 Vision,BoT-SORT Tracker,SAM3 Video Tracker,Camera Focus,Gaze Detection,Segment Anything 2 Model,MoonshotAI Kimi,Single-Label Classification Model,QR Code Detection,Qwen3.5,CogVLM,Object Detection Model,SAM 3 Interactive,Qwen 3.6 API,Bounding Box Visualization,Multi-Label Classification Model,LMM,OpenAI,SAM 3,Image Convert Grayscale,Instance Segmentation Model,EasyOCR,Roboflow Visual Search,Roboflow Dataset Upload,SAM 3,Instance Segmentation Model,Keypoint Detection Model,Pixelate Visualization,Roboflow Dataset Upload,SORT Tracker,Instance Segmentation Model,Track Class Lock,Qwen 3.5 API,Object Detection Model,Anthropic Claude,Polygon Visualization,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Single-Label Classification Model,Seg Preview
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Contrast Enhancement in version v1 has.
Bindings
Example JSON definition of step Contrast Enhancement in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/contrast_enhancement@v1",
"image": "$inputs.image",
"clip_limit": "<block_does_not_provide_example>",
"contrast_multiplier": "<block_does_not_provide_example>",
"normalize_brightness": "<block_does_not_provide_example>"
}