Dehazing algorithm for adaptively corrected transmission under multi-scale morphology

In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging, a new algorithm based on multi-scale morphological reconstruction with adaptive transmittance and atmospheric light correction was proposed. Firstly, the algorithm used the open operation under morphological reconstruction to replace the minimum filter operation in the dark channel, and used the morphological edge to set the scale of the open operation structure elements, and constructed a multi-scale open operation fusion dark channel. After morphological noise reduction, the exact initial transmittance was obtained. According to the relationship between brightness and saturation difference and transmittance, an adaptive transmittance correction model was fitted with Gaussian function to correct the initial transmittance of the sky fog map. Then the local atmospheric light was improved according to the image brightness information and morphology closure operation. Finally, the proposed algorithm was combined with the atmospheric scattering model to obtain an accurate fog free image. The experimental results showed that the proposed algorithm was suitable for fog image restoration under various scenes, the restoration effect was good, and the brightness was suitable.

Authors

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-12-01
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dehazing algorithm for adaptively corrected transmission under multi-scale morphology

YANG Yan, ZHANG Shuai
SHILAP Revista de lepidopterología
Image Enhancement Techniques
article

Dehazing algorithm for adaptively corrected transmission under multi-scale morphology

YANG Yan, ZHANG Shuai
article en

Abstract

In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging, a new algorithm based on multi-scale morphological reconstruction with adaptive transmittance and atmospheric light correction was proposed. Firstly, the algorithm used the open operation under morphological reconstruction to replace the minimum filter operation in the dark channel, and used the morphological edge to set the scale of the open operation structure elements, and constructed a multi-scale open operation fusion dark channel. After morphological noise reduction, the exact initial transmittance was obtained. According to the relationship between brightness and saturation difference and transmittance, an adaptive transmittance correction model was fitted with Gaussian function to correct the initial transmittance of the sky fog map. Then the local atmospheric light was improved according to the image brightness information and morphology closure operation. Finally, the proposed algorithm was combined with the atmospheric scattering model to obtain an accurate fog free image. The experimental results showed that the proposed algorithm was suitable for fog image restoration under various scenes, the restoration effect was good, and the brightness was suitable.

SHILAP Revista de lepidopterología
Sustainable cities and communities
Openalex Percentile: Top 55%
Image Enhancement Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Dehazing algorithm for adaptively corrected transmission under multi-scale morphology — YANG Yan, ZHANG Shuai · SHILAP Revista de lepidopterología (2024) | TGRS Research Map | TGRS