Multidisciplinary Engineering Senior Design Project P 06441 See

Multidisciplinary Engineering Senior Design Project P 06441 See Through Fog Imaging Preliminary Design Review 05/19/06 Project Sponsor: Dr. Rao Team Members: William Parsons, Philip Edwards Team Mentor: Dr. Rao Kate Gleason College of Engineering Rochester Institute of Technology

Objective ► Characterize and optimize two different algorithms for removing fog § Each algorithm uses the same basic equation: ► F(k)=((I(k)/C 0 – 1)*exp(B*dist(k))) + 1 § Equation affects image at the pixel level ► Characterize parameters used to defog § B, C 0, precision, threshold values ► Compare speed, quality, and overall performance of both algorithms

Project Background ► Currently small amount of data published on this process § Basic work to ensure current algorithms perform correctly § Only tested on artificially induced fog § No characterization of parameters

Needs Assessment ► Processing speed § For traveling applications speed is a major factor ► Quality of Image § For surveillance applications quality should be very high ► Overall § § § Performance with different degrees of fog Amount of noise each can handle Accuracy of the constant predictions ► B, C 0

Key Requirements and Critical Parameters ► High quality of defogged images § Low distortion ► Processing time § Must produce images reasonable quickly ►B § Crucial to removing fog from images ► C 0 § Also affects the amount of fog removed § Early estimates suggest it is not as crucial as B

Design Concepts ► Color images § Defog color images instead of black and white ► Hardware Realization § Sample images from a camera, defog images, send to LCD screen ► Analysis of Algorithms § Characterize parameters used in each algorithm § Optimize algorithms to increase overall performance

Chosen Design ► Analysis of the two algorithms § Not enough information known about other designs to make it feasible to design anything else § Determine good initial values for B and C 0 § Determine reasonable threshold values

Chosen Design § Determine reasonable precision needed to correctly approximate constants ►Early tests suggest precision may need to be changed after each run through the algorithms § Determine differences in the two algorithms ►Speed ►Quality ►Size of algorithm § Certain applications may only have a small amount of space available for algorithm

Algorithm Overviews ► Each algorithm attempts to determine some constant values in the equation § B, C 0 for first algorithm, B for the second Each scales the pixels in the fogged image to be between zero and one ► Guess at initial values and then change them to make particular pixel values converge ► § Smallest pixel value to zero and largest pixel value to one ► Subtracts current constant values with previous values and compares with threshold values § If they’re smaller, constants have been found § Otherwise calculate new values

Overall System Design

Removing Induced Fog

Inducing fog in images ► In order to successfully test the algorithms, fog must be artificially induced on images § § § Allows us to know correct B and C 0 values Do not need to determine distance vector, dist(k) Can compare defogged image with original image ► Visual inspection ► Histograms ► Frequency Analysis ► etc

Inducing Fog

Anticipated Design Challenges ► Determining correct amount of precision § Early tests lead us to believe precision will need to be changed each time through the algorithm ► Determining distance vector in natural fog § Images where fog is not induced will have different distance vectors § Need to determine an algorithm to calculate the distance to each pixel ► Determining threshold values for each constant ► Testing with natural fog

Algorithms Precision ► Algorithm comes close to approximating B and C 0 values on first run ► Successive runs show no improvement in either parameter ► Precision value needs to be changed after each run through algorithm

Distance Vector ► Multiplied by B constant in both algorithms ► Greatly affects transformation done on pixel values

Threshold Values ► Determines when B and C 0 have been successfully approximated ► Need to determine when algorithms changes on the pixels is to small to notice ► Also need to determine if this implies the constants are correctly approximated

Preferred Analysis Methods ► Visual Inspection ► Histogram ► Frequency Analysis ► Least Mean Square Error ► Noise Sensitivity

Senior Design II ► Parameter approximation should be close to actual values ► Algorithms should run as quickly as possible ► Defogging of natural fog should be implemented ► Efficient way to determine distance vector in natural fog images should be found

Senior Design II ► Affects of different parameters should be determined ► Comparisons of algorithms overall performance should be made § Processing Speed § Image Quality § Performance with differing amounts of fog

Questions?