Subspace-Based Striping Subspace-Based Striping Noise ReductionNoise Reductionin Hyperspectral Imagesin Hyperspectral Images

Under the Guidance ofP. KRISHNA CHAITANYA M.TECH

C.VISHNU VARDHAN - 08711A0521

K.CHAND BASHA - 08711A0554

G.NAGARJUNA - 08711A0529

M.VENKATESH - 08711A0561

CH.KALYAN KUMAR -08711A0519

G.PRAMOD KUMAR -07711A0597

ABSTRACTABSTRACT A new algorithm for striping noise

reduction in hyper spectral images is proposed.

The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal.

Existing SystemExisting SystemThe existing system available for

fuzzy filters for noise reduction deals with fat-tailed noise like impulse noise.

DRAWBACKS OF EXISTING DRAWBACKS OF EXISTING SYSTEMSYSTEM

Only Impulse noise reduction using fuzzy filters

Gaussian noise is not specially concentrated

It does not distinguish local variation due to noise and due to image structure.

PROBLEM STATEMENTPROBLEM STATEMENT

• The existing system deals with the fat-tailed noise and does not concentrate on gaussian noise.The proposed system must be able to deal with both the medium and narrow – tailed noises

Proposed SystemProposed System

The proposed system presents a new technique for filtering narrow-tailed and medium narrow-tailed noise by a fuzzy filter.

The system, First estimates a “fuzzy derivative” in order to be less sensitive to local variations due to image structures such as edges

Second, the membership functions are adapted accordingly to the noise level to perform “fuzzy smoothing”.

FEASIBILITY REPORTFEASIBILITY REPORTECONOMIC FEASIBILITY

This study is carried out to check the economic impact that the system will have on the organisation

The proposed system is developed utilising freely available technologies.

TECHNICAL FEASIBILITY This study is carried out to check the technical

feasibilty that is technical requirements of system.

The developed system must have a modest requirement as only minimal changes are required for implementing the system.

OPERATIONAL FEASIBILITY The aspect of study is to check the level of

acceptance of the system by the user. The user must not feel threatened by the

system, must accept it as a necessity. The level of confidence must be raised so that

it is able to make constructive criticism, as he is final user of the system.

ALGORITHM SUMMARYALGORITHM SUMMARYSUBSPACE BASED STRIPING

REDUCTION (SBSR) Algorithm

◦SIGNAL SUBSPACE ESTIMATION The signal subspace is estimated from the data,

and the projection matrices for the signal subspace are obtained.

◦STRIPING ESTIMATION The useful signal and the noise realizations for

each band are estimated by applying the projection matrices to the observed data, respectively.

◦STRIPING REDUCTION The destriped image is obtained by

subtracting the striping estimated values from each pixel of the original image

HARDWARE REQUIREMENTSHARDWARE REQUIREMENTS

SYSTEM : Pentium IV 2.4 GHz (min)

HARD DISK : 40 GB (min)RAM : 256 MB (min)

SOFTWARE SOFTWARE REQUIREMENTSREQUIREMENTSOperating system :

Windows XP ProfessionalFront End : JAVA.Tool : Eclipse 3.3

MODULES USEDMODULES USED◦Pre Processing ◦Member function◦Fuzzy Smoothing◦Get Clear Gray Image

MODULE DESCRIPTIONMODULE DESCRIPTIONPre Processing First estimates a “fuzzy

derivative” in order to be less sensitive to local variations due to image structures such as edges

Second, the membership functions are adapted accordingly to the noise level to perform “fuzzy smoothing.”

Member functionFor each pixel that is processed, the first

stage computes a fuzzy derivative. Second, a set of fuzzy rules is fired to determine a correction term. These rules make use of the fuzzy derivative as input.

Fuzzy sets are employed to represent the properties, and while the membership functions for and is fixed, the membership function for are adapted after each iteration.

Fuzzy SmoothingSet the calculated member

function value from processing of gray scale Image to the negative pixel area

Get Clear Gray ImageTo view the clear image by user

this very particular module is used.

REFERENCE:REFERENCE:N.Acito, M.Diani, and G.Corsini,

“Subspace-based striping noise reduction in hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, Vol.49, April 2011.

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