Super-Resolution for Text Images

Super-Resolution ofText Images usingAnt Colony OptimisationProject By,Gowtham Siddarth.D (2010115070)Santhoshkumar.S (2010115101)Satheesh.K (2010115102)Guide : Dr.K.Vani

OBJECTIVETo convert Multiple Low resolution images of a document into a high resolution image .

SCOPERecover original text image from quantization noise and grid-alignment effects that introduce errors in the low-resolution imageAvoid artifacts in the high-resolution image such as blurry edges and rounded cornersSuper resolution address the lack of sharpness in the text image

LITERATURE STUDY S. No:REFERENCE PAPER AND AUTHORDESCRIPTION1.JOINT IMAGE REGISTRATION AND SUPER-RESOLUTION FROM LOW-RESOLUTION IMAGES WITH ZOOMING MOTION by Yushuang Tian and Kim-Hui Yap, Senior Member, IEEE July 2013This paper proposes a new framework for jointimage registration and high-resolution (HR) image reconstruction from multiple low-resolution (LR) observations with zoomingmotion. Conventional super-resolution (SR) methods typically formulate the SR problem as a two-stage process, namely, image registration followed by HR reconstruction2.LEARNING SPATIALLY-VARIABLE FILTERS FOR SUPER- RESOLUTION OF TEXT by Adrian Corduneanu and John C. Platt - 2005The algorithm for super-resolution of text magnifies images in real-time by interpolation with a variable linear filter. The coefficients of the filter are determined nonlinearly from the neighborhood to which it is applied. We train the mapping that defines the coefficients to specifically enhance edges of text, producing a conservative algorithm that infers the detail of magnified text

S. No:REFERENCE PAPER AND AUTHORDESCRIPTION3.ANT COLONY OPTIMIZATION FOR IMAGE REGULARIZATIONBASED ON A NONSTATIONARY MARKOV MODELING by Sylvie Le Hgarat-Mascle, Abdelaziz Kallel, and Xavier Descombes March 2007The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixed-form neighborhood used in classicalMarkov random field regularization techniques4.ANT COLONY OPTIMIZATION BASED FUZZY IMAGE FILTERDESIGN FOR REMOVAL OF IMPULSE NOISES byMin-Chi Kao, Chia-Hung Lin, and Tzuu-Hseng S. Li June 2013The fuzzy system is utilized toimprove the traditional median filter, and an ant colony optimization (ACO) algorithm is used to adjust the parameters of fuzzy image filter and make the filter to achieve betterperformance

S. No:REFERENCE PAPER AND AUTHORDESCRIPTION5.DISCRETE WAVELET TRANSFORM-BASED ANT COLONY OPTIMIZATION FOR EDGE DETECTION by Aminu Muhammad, Ibrahim Bala, Mohammad Shukri Salman and Alaa Eleyan - 2013Ant Colony Optimization (ACO) is used to obtain the edges of an image which is acquired from sampling and quantization of a continuous image. Such techniques generate a pheromone matrix that epresents the edge information at each pixel position on the routes formed by ants dispatched on the image.6.SINGLE-FRAME TEXT SUPER-RESOLUTION: A BAYESIAN APPROACHby Gerald Dalley, Bill Freeman, Joe Marks - 2004given a single image of text . return the image that is generated from a noiseless high-resolution scan. In doing so, we : ( I ) avoid introducing artifacts in the high-resolution image such as blurry edges and rounded corners, (2) recover from quantization noise and grid-alignmont effects that introduce errors in the low-resolution image

ARCHITECTURE

CONTROL POINT REGISTRATIONInput Image 1Input Image 2Select Matching Control pointsEstimate TransformationSolve for Scale and AngleTransform the imageRegistered image

AUTOMATIC REGISTRATIONInput Image 1Input Image 2Feature Detection Using SURF AlgorithmExtract FeaturesMatch the relevant featuresEstimate TransformationRecover original imageRegistered image

FUSION - METHOD 1(Intensity Based Fusion)Registered Image 1 Registered Image 2Intensity 1Intensity 2+Final Fused Imagemin(Intensity 1, Intensity 2)

FUSION METHOD 2 (Discrete Wavelet Transformation)

Registered Image 1 Registered Image 2

+Final Fused ImageLLf(i,j)= ( LL1(i,j) + LL2(i,j) ) / 2LL1LH1HL1HH1LL2LH2HL2HH2

LLfLHfHLfHHfIDWTDWTDWT

Fused ImageIdentify ClassesC2C5Classification Using Decision TreeCalculating similarity between pixelsSOFT CLASSIFICATIONC1- 0 %C2- 25 %C3C1C4C3- 50 %C4- 75 %C5- 100 %Update class labelsArea proportional image

InitializePlace each ant in each pixel in a groupFor each antChoose next pixelFind a pixel of class c1Return to initial pixelUpdate trace level using the tour cost for each antStopping CriteriaFind the pixels with nearest class c1

NoNoYesYes

1. REGISTRATIONImage registrationis the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used incomputer vision,medical imaging, militaryautomatic target recognition, and compiling and analyzing images and data from satellites.Registration is necessary in order to be able to compare or integrate the data obtained from these

When a picture is scanned using the same sensor multiple times, there will be disorientation in the pixel alignment of the images. There are three types of alignment disorder Vertical disorder Horizontal Disorder Angular Disorder

Input ImageInput ImageRegistered Image

STEPS FOR AUTOMATIC REGISTRATIONFind Matching Features Between ImagesDetect features in both imagesExtract feature descriptors Match features by using their descriptorsRetrieve locations of corresponding points for each imageEstimate TransformationSolve for Scale and AngleRecover the Original Image

PSEUDO CODEdo do do for all interest area in given input image, calculate Hessian Matrix H (55) end Identify two interest area with same determinant value; Mark as a feature; end divide the feature (interest area) into 44 subarea; find deviation in x and y axis (estimating transformation); get the angle of deviation as a trace of Hessian matrix; recover the original image by inverse transformation;end

ALGORITHM USEDSURF AlgorithmDetectionAutomatically identify interesting featuresDescriptionEach interest point should have a unique description that does not depend on the features scale and rotation.MatchingGiven and input image, determine which objects it contains, and possibly a transformation of the object, based on predetermined interest points.

1. DETECTION

The determinant of a Hessian Matrix expressed aswhere

DETECTION

The matrix with same value are detected as features

2. DESCRIPTIONThe interest area is divided into 44 subareas that is described by the values of a wavelet response in the x and y directions.

3. MATCHINGMatching in SURF Algorithm is done by

where

Is the trace of Hessian Matrix

TEST CASE

Input Image 1Input Image 2Matched Features

TEST CASE (Cont)

Matching InliersRegistered Image

FUSION METHOD 1Image fusion is the process of combining relevant information from two or more images into a single imageThe resulting image will be more informative than any of the input images

TEST CASE INPUT (Auto Registered)

a)b)

TEST CASE OUTPUT (Auto Registered)

TEST CASE INPUT (Control Point Registered)

a)b)

OUTPUT (Control Point Registered)

COMPARISONFusion of images registered using Automatic Feature Detection is always better than that registered using Control Point Registration

MATHEMATICAL NOTATION FOR FUSIONThe samples are passed through alow pass filter withimpulse response g resulting in a convolutionof the two

The signal is also decomposed simultaneously using ahigh-pass filter. The outputs giving the detail coefficients h and approximation coefficients g

2. SOFT CLASSIFICATION(FUZZY CLASSIFICATION)Multiple images will not have distinct values in a pixel. Pixel information is taken as a vector of multiple classes. For higher resolution of the same image, the vector information can be used to resolve the percentage of different class (black &white)

PSEUDO CODEdo for each pixel use decision tree classification Initialize: Set value for maximum no. of iteration. Set maxItem(i)=-1 for each pixel i. while item