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Histogram

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...

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Related Experiment Video

Updated: Aug 9, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Image Haze Removal Method Based on Histogram Gradient Feature Guidance.

Shiqi Huang¹, Yucheng Zhang², Ouya Zhang¹

¹School of Information Technology & Engineering, Guangzhou College of Commerce, Guangzhou 511363, China.

International Journal of Environmental Research and Public Health

|February 25, 2023

Summary

This summary is machine-generated.

This study introduces a novel method for enhancing optical remote sensing images degraded by haze. The histogram gradient feature guidance (HGFG) method effectively removes haze, improving image clarity and detail preservation.

Keywords:

dark channel prior method gradient feature guided filtering haze removal remote sensing image

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Area of Science:

Remote Sensing
Image Processing
Computer Vision

Background:

Haze significantly degrades optical remote sensing image quality, causing low contrast, blurred details, and color distortion.
Effective haze removal is crucial for accurate analysis and application of remote sensing data.

Purpose of the Study:

To develop an advanced image haze removal technique for optical remote sensing images.
To improve image clarity, contrast, and detail retention in hazy conditions.

Main Methods:

Proposed a novel image haze removal method based on histogram gradient feature guidance (HGFG).
Utilized multidirectional gradient features and guided filtering to refine the atmospheric transmittance map.
Incorporated adaptive regularization parameters for effective haze removal.

Main Results:

Experimental results demonstrate significant improvements in image definition and contrast.
The HGFG method effectively preserves fine details and color fidelity.
Validated across diverse remote sensing image datasets.

Conclusions:

The proposed HGFG method offers a robust solution for haze removal in optical remote sensing.
The technique exhibits strong haze removal capabilities, detail preservation, and broad applicability.
High application value for remote sensing image preprocessing.