maxcrous / dehazing_using_color_lines

An implementation of Fattal's 'Dehazing using Color-Lines'

Dehazing using Color-lines

This repository contains an open-source implementation of Fattal's algorithm for dehazing. It is meant as an accessible implementation with low complexity and ample documentation that can be used as an aid for studying the algorithm.

Dehazing a single image with linear interpolation takes around 10 seconds on a modern CPU (previously 60 seconds). This is achieved by using Numba in the main loop. Speed is improved even further by Numba when dehazing multiple images successively.

For other implementations, see those by Ekesium and Tomlk (also pixel-wise inference, but in scala and C++).

How to Use

Use a venv and install the required packages by running pip install -r requirements.txt.
Execute python dehaze.py to dehaze images/bricks.png.

Example output

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To find other images with a given airlight vector, see the paper's result page.

File structure

Name	Purpose
dehaze.py	Main function
ransac.py	RANSAC algorithm for evaluating color-line hypotheses
window.py	A windowing class for concise tiling
color_line.py	Functions for evaluating color-line validity
constants.py	All constants and thresholds (comments note where values deviate from Fattal)
linear_interpolation.py	Linear interpolation of the transmission image
markov_random_field.py	Markov random field interpolation and smoothing functions (autograd optimization)
recover.py	Recovers the dehazed pixel values given the estimated transmission map
numba_patch.py	Patches the Numba's jitclass, as it currently does not support the numpy error model

TODO

• Implement automatic airlight recovery paper