WO2000060532A1 - Method and apparatus for restoration of low resolution images - Google Patents
Method and apparatus for restoration of low resolution images Download PDFInfo
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- WO2000060532A1 WO2000060532A1 PCT/US2000/008494 US0008494W WO0060532A1 WO 2000060532 A1 WO2000060532 A1 WO 2000060532A1 US 0008494 W US0008494 W US 0008494W WO 0060532 A1 WO0060532 A1 WO 0060532A1
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- 230000003362 replicative effect Effects 0.000 claims 2
- 238000012015 optical character recognition Methods 0.000 abstract description 31
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- 238000009826 distribution Methods 0.000 abstract description 11
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20192—Edge enhancement; Edge preservation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30176—Document
Definitions
- TITLE METHOD AND APPARATUS FOR RESTORATION OF LOW RESOLUTION IMAGES INVENTORS: Paul THOUIN and Chein-I CHANG
- the present invention relates in general to image processing, and more particularly to creating a higher resolution image from a lower resolution image.
- the invention described and claimed herein comprises a novel method and apparatus for improving the definition of a low- resolution image by taking advantage of characteristics of its bimodal distribution so as to produce an expanded image with improved definition.
- This can not only reconstruct image information deteriorated by low-resolution devices, but also can refine original low spatial resolution images so as to significantly improve- image quality.
- An image is improved by iteratively solving a nonlinear optimization problem using a preferred Bimodal-Smooth-Average scoring method.
- the applications for the technique are improved optical character recognition, restoration of binary images and video frames.
- the terms “low” and “high” are comparative only: they refer to the comparison between two images and are not meant to imply any absolute degree of resolution.
- image acquisition consists of converting a continuous image into discrete values obtained from a group of sensor elements. While generally cheaper to produce and transmit, a low resolution imaging system generally produces a less accurate representation of the continuous image than would a high resolution imaging system.
- Common methods of expanding low resolution images to high resolution images by interpolation typically smooth over important details in the process. Linear interpolation tends to smooth image data at transition regions, resulting in blurry images. Cubic spline expansion allows for sharp transitions, but tends to produce a ringing effect at discontinuities.
- Document images are typically acquired by a scanner, whereas video frames are most often captured by a digital camera.
- the image acquisition process in both cases consists of converting a continuous image into discrete values obtained from a group of sensor elements. Each sensor element produces a value which is a function of the amount of light incident on the device.
- the allowable range For 8-bit grayscale quantization, the allowable range
- the sensors are typically arranged in a non-overlapping grid of square elements, smaller elements result
- Text image resolution expansion has become increasingly important in a number of areas of image processing.
- OCR Optical Character Recognition
- Digital video compression algorithms can also benefit from successful text resolution expansion techniques.
- Common methods of interpolation which were not designed specifically for text images, typically smooth over the important details and produce inadequate expansion. This paper proposes a new nonlinear restoration technique for text images, which creates smooth foreground and background regions while preserving sharp edge transitions.
- the proposed method computes resolution expansion using an algorithm specifically
- the goal of resolution expansion is to create an expanded image with improved defi ⁇
- BSA Bimodal-Smooth-Average
- a principal feature of the invention is the iterative solution of a nonlinear optimization problem using a preferred Bimodal- Smooth-Average scoring method.
- Figure 1 is a schematic of a system suitable for carrying out the invention.
- Figure 2 is a block diagram of the text resolution expansion system of the invention.
- FIG. 3 is a flow chart of the BSA process of the invention.
- Figure 4 is an illustration of the results of a BSA restoration experiment, showing the progressive improvement of resolution with iterations.
- Figures 5 illustrates the performance of the BSA process as compared to prior art techniques of linear interpolation and cubic spline in grayscale document restoration and grayscale video frame restoration, respectively.
- Figures 6 and 7 illustrate the performance of optical character recognition following cubic spline ( Figure 6) versus BSA ( Figure 7) image enhancement .
- Figure 8 compares OCR accuracy of spline vs. BSA for gray-level images and binary document images.
- Figure 9 illustrates experimental results of binary document restoration.
- FIGS 10 and 11 illustrate experimental results of restoration of video frames.
- the invention is a novel method and apparatus which improves the definition of a low-resolution image by taking advantage of characteristics of its bimodal distribution so as to produce an expanded image with improved definition. This can not only reconstruct image information deteriorated by low-resolution devices, but also can refine original low spatial resolution so as to significantly improve image quality.
- the invention may be illustrated in the context of an image acquisition system (1) providing image data (10) which serves as an input to a general purpose computer (100) programmed to carry out the following processes.
- Image acquisition system (1) comprises a plurality of image sensors (2) which generate image data (10) concerning the image.
- This image data (10) serves as input to computer (100) via input means (101) .
- Computer (100) includes processing means (102) , programmed to execute the steps set forth herein so as to generate a high resolution image, which may be displayed or otherwise used via output means (103) .
- a text resolution expansion system receives low-resolution image (10) , preprocesses the image in preprocessing module (201) and outputs the result to bimodal estimation module (202); the bimodal estimation module (202) processes the data and outputs the result to resolution expansion module (203) for pixel replication and output to BSA restoration module (204) where the image is partitioned into overlapping blocks prior to BSA restoration; the BSA restoration module then outputs the result to a post-processing module (205) which provides an expanded resolution image (206) for display or other use.
- the operation of the BSA restoration module (203) is illustrated in greater detail in Figure 3.
- An initial image (301) is provided and an initial BSA score is computed (302) ; an image update is then computed (303) so as to produce an updated image (304) .
- a new BSA score is computed for the updated image (305) and compared to the prior BSA score (306) ; if the new score does not represent an improvement over the prior score by a predetermined amount, the restoration process is complete; otherwise, another image update is computed (303) and the process continues iteratively until the improvement is below the predetermined amount.
- the image acquisition process consists of converting a continuous image into discrete values obtained from a group of sensor elements. Each sensor element produces a value which is a function of the amount of light incident on the device. For 8-bit grayscale quantization, the allowable range of values for each sensor are integers from 0 (black) to 255 (white).
- the sensors are typically arranged in a non-overlapping grid of square
- the problem 1 is to restore the high-resolution image HI qTi qc given only the low-resolution image / r , c , where r and c are thS ⁇ taBef dWC-ws an ' tf columns in the low-resolution image and q is the resolution expansion factor.
- the image acquisition process of obtaining LI rfi from i-7, r ,.c is 6 iven bv
- Eq. (1) represents a typical image restoration problem where we are required to restore the i-7, r ,,. based on the observed LI Tfi via the . relationship described by this equation. Since there are a great number of high-resolution images which may satisfy the constraint of the observed low-resolution image given by Eq. (1), image restoration is generally an ill-posed inverse problem.
- a low-resolution image is initially input to the Preprocessing Module where binary and color images are converted to grayscale images.
- the image histogram is computed by the Bimodal Estimation Module to estimate the means of the black and white pixel distributions.
- the initial image expansion is performed by the Resolution Expansion Module which uses pixel replication to create a high-resolution image from the low-resolution original.
- the image is divided into overlapping blocks of pixels which are restored independently by the BSA algorithm in the BSA Restoration Module.
- the Postprocessing Module performs a grayscale-to-binary conversion.
- the next section details the image expansion system for grayscale images, followed by a section on binary image expansion.
- the peak of the white distribution ⁇ w and the peak of the black distribution ⁇ B are estimated from the calculated histogram. These values are required
- Pixel replication where the value of each high-resolution pixel is
- Postprocessing Module converts the high-resolution grayscale image to a binary image if a binary expanded image is desirable.
- the BSA Restoration Module converts the high-resolution grayscale image to a binary image if a binary expanded image is desirable.
- the restoration process can be summarized as follows.
- initial score BSAQ is computed from the original image Jo- At each iteration, the image
- the scoring function used by the BSA Restoration process is designed to measure how
- This function referred to as the BSA scoring function, is expressed as the weighted sum
- the BSA score is defined by
- ⁇ ⁇ , ⁇ 2 , and ⁇ 3 are Lagrange multipliers and x is a block of pixels to be restored.
- a block x is defined both as a group of 4 x 4 low-resolution
- restoration process is to iteratively solve for the block of pixels x that minimizes the
- the typical distribution of a text image contains two peaks, a large one at ⁇ u , / .. te -
- Partial derivatives of the bimodal score B(x) are independent of neighboring pixels.
- the estimated means of the bimodal distribution, ⁇ bi k and ⁇ Wh ite, are known a priori and
- r and c are the row and column indices within the block being evaluated.
- the first and second partial derivatives are calculated in a straightforward manner.
- the first partial derivative of this smoothing score with respect to pixel x r,c is
- the second partial derivatives are nonzero only when
- smoothness score S(x) is a function of the four neighboring pixels.
- the second partial derivatives are non-zero only with respect to their corresponding neighbors.
- an average score A(x) is used to measure how
- Fig. 5 shows four low-resolution pixels whose values
- ⁇ i is the value of each low-resolution
- pixel, and x ⁇ c are the restored high-resolution pixels corresponding to pixel ⁇ j.
- initial high-resolution image formed by using pixel replication always has an average score of zero because it satisfies the constraint.
- the first partial derivative for the group of high-resolution pixels corresponding to pixel ⁇ i is equal to
- the goal of the restoration algorithm is to solve for the image block that minimizes
- a block x is defined both as a group of 4 x 4 low-resolution pixels and as the 4q x 4q high-resolution
- the 4 4 size was specifically chosen because it contains enough pixels to adequately measure text characteristics but is not too large
- the goal of resolution enhancement is to create a
- Pixel replication where every value within a q x q neighborhood is identical to the corresponding low-resolution pixel, is used for the initial expansion.
- Each 4 ⁇ x 4q block of high-resolution pixels is restored independently using iterative optimization techniques
- the first and second partial derivatives of the BSA scoring function are used to determine the image update. To avoid block boundary discontinuities only the center
- each 4q x 4 ⁇ block of pixels x is converted to a (4g) 2 -long vector x using
- a small distance away from x the BSA function can be represented by its second order
- the (4g) 2 x (4q) 2 Hessian given below by Eq. (15) is the symmetric matrix of mixed partial second derivatives, which shows how a change in two variables affects the BSA function.
- Fig. 7 is the Hessian matrix for an 8 x 8 block of pixels. Because of the neighborhood dependence
- VBS-4'(x) [VBSA ⁇ x) ⁇ E (18)
- ABSA [VBSA' ⁇ x))[ ⁇ ') + (21)
- the functional minimum is achieved by stepping in the direction
- BSA score produces a restored image that is the optimal combination of these bimodal, smoothness, and average measures.
- Cubic spline expansion [12] approximates the given discrete low-resolution pixels as a smooth continuous curve ob ⁇
- the second experiment involved scanning low-resolution document images, expanding the images with the various
- Fig. w shows resulting images obtained from linear interpolation, cubic spline
- Fig. (c) depicts the resulting image from cubic spline expansion which has better con ⁇
- the image obtained using BSA restoration in Fig. tf'Cd) has excellent contrast and sharp edges and is supe ⁇ oc to the images obtained using other interpolation methods for this example.
- Fig. _-- (d) has excellent contrast and sharp edges and is superior to the images obtained using other interpolation methods for this example.
- mean squared error was used to compare the various methods of image resolution expansion.
- the definition of mean squared is
- each original binary image is first convolved with a spatial mask in the Preprocessing a. eO
- a global threshold Tbi naT y is used in the Postprocessing Module
- This threshold is computed to
- the first step of the binary restoration process is to convert the original low-resolution
- Convolution is performed to create gray values from a group of neighboring binary pixels.
- R is the number of rows and C is the number of columns within each image.
- WM is the relative weight for the middle pixel
- ws represents the four side pixels, and wc corresponds to the weight for each of the four
- Spatial convolution can be thought of as a weighted averaging over a neighborhood of
- grayscale pixels x g ⁇ ay are easily computed from the original binary pixel x r , c along with its eight
- the binary pixel is black, then the grayscale pixel should be dark as well. Specifically,
- ⁇ 4» ,,- «,e l ⁇ s(lr-l,e + S r + l.e + ⁇ r,c-l + Ir,e+l) + ( 1 ) tUc(Xr-l,c- l + Ir-l,c+l + ⁇ ⁇ + ⁇ c-1 + ⁇ ⁇ + ⁇ c+l )j
- the range of this measure is 0 ⁇ ⁇ ⁇ 255.
- the computed grayscale pixel x s ⁇ ay is then de t ermined based on the original value of the binary image x in the following manner
- panded images produced by our system were compared with 300 dpi images created by
- this resolution expansion system is to improve the OCR accuracy- Real-time image processing is not a requirement.
- Our system takes approximately 10 minutes to expand a single full-page 100 dpi document image by a factor of 3 on a 250 MHz workstation- Cubic spline interpolation is much faster than our system and takes about 10 seconds for a single document page.
- Video frames are typically smaller than low-resolution document images and are therefore processed more quickly by the system.
- a 320 x 240 video frame takes approximately 2 minutes for our system to expand by a factor of three. Cubic spline expansion of a video frame only takes about 2 seconds. If the speed concern becomes an issue in the future, our approach has a potential for massive parallelization because the images can be divided into blocks of pixels which can be restored independently.
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AU40523/00A AU4052300A (en) | 1999-04-01 | 2000-03-30 | Method and apparatus for restoration of low resolution images |
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2005013256A2 (en) * | 2003-07-31 | 2005-02-10 | Hewlett-Packard Development Company, L.P. | Generating and alternately displaying spatially offset sub frames |
WO2005101368A1 (en) * | 2004-04-08 | 2005-10-27 | Hewlett-Packard Development Company, L.P. | Generating and displaying spatially offset sub-frames to provide higher resolution image |
US8130304B2 (en) | 2009-07-24 | 2012-03-06 | Aptina Imaging Corporation | Image sensors with pixel charge summing |
TWI406187B (en) * | 2010-08-19 | 2013-08-21 | Univ Nat Cheng Kung | Fast and high quality image/video interpolation method and apparatus |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5586196A (en) * | 1991-04-24 | 1996-12-17 | Michael Sussman | Digital document magnifier |
US5611023A (en) * | 1992-09-02 | 1997-03-11 | Ricoh Company, Ltd. | Apparatus and method for processing two-tone image data so as to smooth and magnify image |
US5650858A (en) * | 1992-08-26 | 1997-07-22 | Hewlett-Packard Company | Pixel image edge-smoothing method and system |
US5991448A (en) * | 1994-10-28 | 1999-11-23 | Oki Electric Industry Co., Ltd. | Image encoding and decoding method and apparatus using edge synthesis and inverse wavelet transform |
-
2000
- 2000-03-30 WO PCT/US2000/008494 patent/WO2000060532A1/en active Application Filing
- 2000-03-30 AU AU40523/00A patent/AU4052300A/en not_active Abandoned
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5586196A (en) * | 1991-04-24 | 1996-12-17 | Michael Sussman | Digital document magnifier |
US5650858A (en) * | 1992-08-26 | 1997-07-22 | Hewlett-Packard Company | Pixel image edge-smoothing method and system |
US5611023A (en) * | 1992-09-02 | 1997-03-11 | Ricoh Company, Ltd. | Apparatus and method for processing two-tone image data so as to smooth and magnify image |
US5991448A (en) * | 1994-10-28 | 1999-11-23 | Oki Electric Industry Co., Ltd. | Image encoding and decoding method and apparatus using edge synthesis and inverse wavelet transform |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2005013256A2 (en) * | 2003-07-31 | 2005-02-10 | Hewlett-Packard Development Company, L.P. | Generating and alternately displaying spatially offset sub frames |
WO2005013256A3 (en) * | 2003-07-31 | 2005-03-24 | Hewlett Packard Development Co | Generating and alternately displaying spatially offset sub frames |
US7289114B2 (en) | 2003-07-31 | 2007-10-30 | Hewlett-Packard Development Company, L.P. | Generating and displaying spatially offset sub-frames |
WO2005101368A1 (en) * | 2004-04-08 | 2005-10-27 | Hewlett-Packard Development Company, L.P. | Generating and displaying spatially offset sub-frames to provide higher resolution image |
GB2429365A (en) * | 2004-04-08 | 2007-02-21 | Hewlett Packard Development Co | Generating and displaying spatially offset sub-frames to provide higher resolution image |
US8130304B2 (en) | 2009-07-24 | 2012-03-06 | Aptina Imaging Corporation | Image sensors with pixel charge summing |
TWI406187B (en) * | 2010-08-19 | 2013-08-21 | Univ Nat Cheng Kung | Fast and high quality image/video interpolation method and apparatus |
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WO2000060532A9 (en) | 2002-06-20 |
AU4052300A (en) | 2000-10-23 |
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