Contrast Equalization

Class: ContrastEqualizationBlockV1

Source: inference.core.workflows.core_steps.classical_cv.contrast_equalization.v1.ContrastEqualizationBlockV1

Enhance image contrast using configurable equalization methods (Contrast Stretching, Histogram Equalization, or Adaptive Equalization) to improve image visibility, distribute pixel intensities more evenly, and enhance details in low-contrast or poorly lit images for preprocessing, enhancement, and quality improvement workflows.

How This Block Works

This block enhances image contrast by redistributing pixel intensities using one of three equalization methods. The block:

1. Receives an input image to enhance with contrast equalization
2. Selects the contrast equalization method based on equalization_type parameter
3. Applies the selected equalization method:

For Contrast Stretching: - Calculates the 2nd and 98th percentiles of pixel intensities in the image (finds the darkest and brightest meaningful values, ignoring extreme outliers) - Stretches the intensity range between these percentiles to span the full 0-255 range - Enhances contrast by expanding the dynamic range while preserving relative intensity relationships - Useful for images with a narrow intensity range that need stretching to full range

For Histogram Equalization: - Normalizes pixel intensities to 0-1 range for processing - Computes and equalizes the image histogram to create a uniform distribution of pixel intensities - Redistributes pixel values so that each intensity level has approximately equal frequency - Scales the equalized values back to 0-255 range - Enhances contrast globally across the entire image, improving visibility of features

For Adaptive Equalization: - Normalizes pixel intensities to 0-1 range for processing - Applies adaptive histogram equalization (CLAHE - Contrast Limited Adaptive Histogram Equalization) - Divides the image into small regions and equalizes each region independently - Uses clip_limit=0.03 to limit contrast enhancement and prevent over-amplification of noise - Combines local equalized regions using bilinear interpolation for smooth transitions - Scales the result back to 0-255 range - Enhances contrast adaptively, preserving local details while improving overall visibility

1. Preserves image metadata from the original image
2. Returns the enhanced image with improved contrast

The block provides three methods with different characteristics: Contrast Stretching expands intensity ranges linearly, Histogram Equalization creates uniform intensity distribution globally, and Adaptive Equalization enhances contrast locally while preventing over-amplification. Each method works best for different scenarios - Contrast Stretching for images with narrow intensity ranges, Histogram Equalization for overall contrast improvement, and Adaptive Equalization for images with varying contrast across regions.

Common Use Cases

• Image Preprocessing for Models: Enhance image contrast before feeding to detection or classification models (e.g., improve contrast before object detection, enhance visibility before classification, prepare images for model processing), enabling improved model performance workflows
• Low-Contrast Image Enhancement: Improve visibility and details in low-contrast or poorly lit images (e.g., enhance dark images, improve visibility in low-light conditions, reveal details in low-contrast scenes), enabling image enhancement workflows
• Detail Enhancement: Reveal hidden details in images with poor contrast (e.g., enhance details in shadow regions, reveal features in dark areas, improve visibility of subtle details), enabling detail enhancement workflows
• Image Quality Improvement: Improve overall image quality and visibility (e.g., enhance overall image quality, improve visibility for analysis, optimize images for display), enabling image quality workflows
• Medical and Scientific Imaging: Enhance contrast in medical or scientific images for better analysis (e.g., enhance medical imaging contrast, improve scientific image visibility, prepare images for analysis), enabling scientific imaging workflows
• Document Image Enhancement: Improve contrast in scanned documents or document images (e.g., enhance document contrast, improve text visibility, optimize scanned documents), enabling document enhancement workflows

Connecting to Other Blocks

This block receives an image and produces an enhanced image with improved contrast:

• After image input blocks to enhance input images before further processing (e.g., enhance contrast in camera feeds, improve visibility in image inputs, optimize images for workflow processing), enabling image enhancement workflows
• Before detection or classification models to improve model performance with better contrast (e.g., enhance images before object detection, improve visibility for classification models, prepare images for model analysis), enabling enhanced model input workflows
• After preprocessing blocks to apply contrast enhancement after other preprocessing (e.g., enhance contrast after filtering, improve visibility after transformations, optimize images after preprocessing), enabling multi-stage enhancement workflows
• Before visualization blocks to display enhanced images with better visibility (e.g., visualize enhanced images, display improved contrast results, show enhancement effects), enabling enhanced visualization workflows
• Before analysis blocks that benefit from improved contrast (e.g., analyze enhanced images, process improved visibility images, work with optimized contrast), enabling enhanced analysis workflows
• In image quality improvement pipelines where contrast enhancement is part of a larger enhancement workflow (e.g., enhance images in multi-stage pipelines, improve quality through enhancement steps, optimize images in processing chains), enabling image quality pipeline workflows

Type identifier

Use the following identifier in step "type" field: roboflow_core/contrast_equalization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
equalization_type	str	Type of contrast equalization method to apply: 'Contrast Stretching' stretches the intensity range between 2nd and 98th percentiles to full 0-255 range (linear expansion, good for narrow intensity ranges), 'Histogram Equalization' (default) creates uniform intensity distribution globally (equalizes histogram across entire image, good for overall contrast improvement), or 'Adaptive Equalization' enhances contrast locally in small regions while limiting over-amplification (CLAHE with clip_limit=0.03, good for images with varying contrast). Default is 'Histogram Equalization' which provides good general-purpose contrast enhancement. Choose based on image characteristics and enhancement needs..	✅

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 Equalization in version v1.

• inputs: Clip Comparison, Florence-2 Model, Morphological Transformation, Google Gemini, LMM, Instance Segmentation Model, Polygon Zone Visualization, Email Notification, Keypoint Visualization, Roboflow Custom Metadata, Camera Focus, Anthropic Claude, Multi-Label Classification Model, Image Threshold, LMM For Classification, Keypoint Detection Model, Anthropic Claude, Email Notification, Reference Path Visualization, Stitch OCR Detections, Camera Focus, Image Slicer, Stability AI Image Generation, Stability AI Outpainting, Stitch Images, Blur Visualization, OpenAI, Roboflow Dataset Upload, Depth Estimation, Google Gemini, CogVLM, Image Preprocessing, Local File Sink, Florence-2 Model, Image Convert Grayscale, Dynamic Crop, Dot Visualization, Triangle Visualization, OCR Model, Crop Visualization, Twilio SMS Notification, Perspective Correction, Twilio SMS/MMS Notification, EasyOCR, Grid Visualization, Google Gemini, Trace Visualization, QR Code Generator, Pixelate Visualization, OpenAI, Camera Calibration, Roboflow Dataset Upload, Webhook Sink, Single-Label Classification Model, Object Detection Model, VLM as Detector, Background Subtraction, Bounding Box Visualization, Contrast Equalization, Halo Visualization, Model Comparison Visualization, Label Visualization, Slack Notification, OpenAI, Circle Visualization, Image Contours, Background Color Visualization, Image Blur, Mask Visualization, VLM as Classifier, Google Vision OCR, Llama 3.2 Vision, Color Visualization, Corner Visualization, Classification Label Visualization, OpenAI, Line Counter Visualization, Ellipse Visualization, Icon Visualization, Model Monitoring Inference Aggregator, Image Slicer, Absolute Static Crop, Polygon Visualization, SIFT Comparison, Stability AI Inpainting, Relative Static Crop, SIFT, CSV Formatter, Text Display
• outputs: Instance Segmentation Model, Clip Comparison, Florence-2 Model, Morphological Transformation, Google Gemini, LMM, Instance Segmentation Model, Motion Detection, Email Notification, Detections Stitch, Polygon Zone Visualization, Keypoint Visualization, Camera Focus, Anthropic Claude, Multi-Label Classification Model, Pixel Color Count, Image Threshold, LMM For Classification, Keypoint Detection Model, Anthropic Claude, Gaze Detection, Reference Path Visualization, Camera Focus, Stability AI Image Generation, Stitch Images, Stability AI Outpainting, Image Slicer, SmolVLM2, OpenAI, Roboflow Dataset Upload, Depth Estimation, YOLO-World Model, Google Gemini, CogVLM, Image Preprocessing, VLM as Detector, Florence-2 Model, Image Convert Grayscale, SAM 3, Byte Tracker, Dynamic Crop, Time in Zone, Perception Encoder Embedding Model, Moondream2, Triangle Visualization, Dot Visualization, OCR Model, Seg Preview, Crop Visualization, Twilio SMS/MMS Notification, Perspective Correction, EasyOCR, SAM 3, Google Gemini, Object Detection Model, Text Display, Trace Visualization, Pixelate Visualization, OpenAI, CLIP Embedding Model, Camera Calibration, Roboflow Dataset Upload, Buffer, Barcode Detection, Object Detection Model, Single-Label Classification Model, QR Code Detection, VLM as Detector, Background Subtraction, Bounding Box Visualization, Contrast Equalization, Model Comparison Visualization, Halo Visualization, Label Visualization, OpenAI, Circle Visualization, Qwen2.5-VL, Image Contours, Image Blur, Background Color Visualization, Mask Visualization, Dominant Color, VLM as Classifier, Google Vision OCR, Llama 3.2 Vision, Color Visualization, Corner Visualization, Classification Label Visualization, Single-Label Classification Model, OpenAI, Segment Anything 2 Model, Clip Comparison, Template Matching, Line Counter Visualization, Icon Visualization, Ellipse Visualization, Image Slicer, Detections Stabilizer, Absolute Static Crop, VLM as Classifier, Polygon Visualization, SIFT Comparison, Stability AI Inpainting, Qwen3-VL, SAM 3, Keypoint Detection Model, Relative Static Crop, SIFT, Blur Visualization, Multi-Label Classification Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Contrast Equalization in version v1 has.

Bindings

• input

  • image (image): Input image to enhance with contrast equalization. The block applies one of three contrast equalization methods based on the equalization_type parameter. Works on color or grayscale images. The enhanced image will have improved contrast, better visibility, and enhanced details. Original image metadata is preserved in the output..
  • equalization_type (string): Type of contrast equalization method to apply: 'Contrast Stretching' stretches the intensity range between 2nd and 98th percentiles to full 0-255 range (linear expansion, good for narrow intensity ranges), 'Histogram Equalization' (default) creates uniform intensity distribution globally (equalizes histogram across entire image, good for overall contrast improvement), or 'Adaptive Equalization' enhances contrast locally in small regions while limiting over-amplification (CLAHE with clip_limit=0.03, good for images with varying contrast). Default is 'Histogram Equalization' which provides good general-purpose contrast enhancement. Choose based on image characteristics and enhancement needs..

• output

  • image (image): Image in workflows.

Example JSON definition of step Contrast Equalization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/contrast_equalization@v1",
    "image": "$inputs.image",
    "equalization_type": "Histogram Equalization"
}