Patent Publication Number: US-8538140-B2

Title: Device and method for detecting whether an image is blurred

Description:
BACKGROUND 
     Cameras are commonly used to capture an image of a scene that includes one or more objects. Unfortunately, some of the images are blurred. For example, movement of the camera and/or movement of the objects in the scene during the exposure time of the camera can cause the image to be blurred. Further, if the camera is not properly focused when the image is captured, that image will be blurred. 
     Currently, there are certain methods that are used to determine whether the image is blurred. Unfortunately, many of these methods associate blur degree with edge spreading in the image. As a result thereof, their performance is rather sensitive to the accuracy of edge detection techniques. For a sharp and low noise image, the current edge detection techniques can achieve relatively good results. However, this performance degrades significantly for other types of images. For example, it has been complicated to attain robust performance for specific image types, such as macros, close-up portraits and night scenes. Additionally, it has also been difficult to achieve robust performance for large scale testing sets that cover multiple image types and various camera settings. 
     SUMMARY 
     The present invention is directed to a method for detecting or predicting whether a test image is blurred. In one embodiment, the method includes extracting a training statistical signature that is based on a plurality of data features from a training image set, the training image set including sharp images and blurry images; training a classifier to discriminate between the sharp images and the blurry images based on the training statistical signature; and applying the trained classifier to a test image that is not included in the training image set to predict whether the test image is sharp or blurry. 
     In one embodiment, the step of extracting includes decomposing one of the images in the training image set using a multi-scale image decomposition that includes a plurality of levels, such as a Laplacian pyramid, a steerable pyramid or a wavelet pyramid. 
     In another embodiment, the step of extracting includes measuring one or more statistical moments for at least one level of the Laplacian pyramid. These statistical moments can include one or more of (i) a mean, (ii) a standard deviation, (iii) skewness, and (iv) kurtosis. 
     In some embodiments, the step of extracting includes estimating a covariance between level N and level N+1 of the Laplacian pyramid. 
     In certain embodiments, the step of extracting includes extracting a metadata feature of at least one of the images. In one such embodiment, the metadata features can include one or more of a focal length, an f-number, an ISO sensitivity, an exposure time, a flash, an exposure value and a handholdable factor (X). 
     In one embodiment, the step of training includes training a non-linear support vector machine on the training statistical signature of the training image set. Alternatively, the step of training can include training a linear discriminant analysis on the training statistical signature of the training image set. 
     In certain embodiments, the step of applying includes extracting a testing statistical signature from the test image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features of this invention, as well as the invention itself, both as to its structure and its operation, will be best understood from the accompanying drawings, taken in conjunction with the accompanying description, in which similar reference characters refer to similar parts, and in which: 
         FIG. 1  is a simplified view of a scene, an image apparatus having features of the present invention, a raw, first captured image of the scene, a raw, second captured image of the scene, and an adjusted image; 
         FIG. 2  is a simplified front perspective view of the image apparatus in  FIG. 1 ; 
         FIG. 3  is a flow chart that illustrates one embodiment of an image classification procedure having features of the present invention; 
         FIG. 4A  is a schematic diagram that illustrates a portion of a first series of steps of a Laplacian pyramid used for one embodiment of the image classification procedure outlined in  FIG. 3 ; 
         FIG. 4B  is a schematic diagram that illustrates a portion of an additional series of steps of the Laplacian pyramid outlined in  FIG. 4A ; 
         FIG. 4C  is a schematic diagram that illustrates five levels of the Laplacian pyramid outlined in  FIGS. 4A and 4B ; 
         FIG. 5A  is a flow chart that illustrates one embodiment of a portion of the image classification procedure outlined in  FIG. 3 ; 
         FIG. 5B  is a flow chart that illustrates one embodiment of another portion of the image classification procedure outlined in  FIG. 3 ; 
         FIG. 5C  is a flow chart that illustrates one embodiment of yet another portion of the image classification procedure outlined in  FIG. 3 ; 
         FIG. 5D  is a flow chart that illustrates one embodiment of still another portion of the image classification procedure outlined in  FIG. 3 ; 
         FIG. 6  is a flow chart that illustrates another embodiment of an image classification procedure having features of the present invention; 
         FIG. 7A  is a flow chart that describes one embodiment for a collection of image features from the embodiment illustrated in  FIG. 6 ; 
         FIG. 7B  is a flow chart that describes one embodiment for an LDA training from the embodiment illustrated in  FIG. 6 ; and 
         FIG. 7C  is a flow chart that describes one embodiment for an LDA prediction from the embodiment illustrated in  FIG. 6 . 
     
    
    
     DESCRIPTION 
       FIG. 1  is a simplified perspective view of an image apparatus  10  having features of the present invention, and a scene  12 .  FIG. 1  also illustrates a raw first captured image  14  (illustrated away from the image apparatus  10 ), and a raw second captured image  16  (illustrated away from the image apparatus  10 ), each captured by the image apparatus  10 . In  FIG. 1 , the first captured image  14  is intended to illustrate a sharp image  18  (including non-wavy lines) and the second captured image  16  is intended to illustrate image blur  20  (including wavy lines). For example, movement of the image apparatus  10 , and/or movement of an object  22  in the scene  12  during the capturing of the blurred image  16  can cause motion blur  20  in the image  14 . Additionally, or in the alternative, blur  20  in the image  16  can be caused by the image apparatus  10  not being properly focused when the image  14  is captured. 
     In one embodiment, as provided herein, the image apparatus  10  includes a control system  24  (illustrated in phantom) that uses one or more unique methods for detecting if one or more of the captured images  14 ,  16  are blurred beyond a predetermined threshold that can be established by the user. As a result of using the method(s) to evaluate each image  14 ,  16 , the present invention provides a device and method for determining whether a particular image  14 ,  16  is blurred or sharp with improved accuracy. Subsequently, a deblurring process can be applied to only the images  16  that are determined to be blurred. Thus, a sharp image  14  will not be unnecessarily subjected to the deblurring process. 
     The type of scene  12  captured by the image apparatus  10  can vary. For example, the scene  12  can include one or more objects  22 , e.g. animals, plants, mammals, and/or environments. For simplicity, in  FIG. 1 , the scene  12  is illustrated as including one object  22 . Alternatively, the scene  12  can include more than one object  22 . In  FIG. 1 , the object  22  is a simplified stick figure of a person, although it is recognized that the object  22  in  FIG. 1  can be any object  22  and is representative of any suitable image. For instance, the scenes  12  can include macros, close-up portraits and/or night scenes, as non-exclusive examples. 
       FIG. 2  illustrates a simplified, front perspective view of one non-exclusive embodiment of the image apparatus  10 . In this embodiment, the image apparatus  10  is a digital camera, and includes an apparatus frame  236 , an optical assembly  238 , and a capturing system  240  (illustrated as a box in phantom), in addition to the control system  24  (illustrated as a box in phantom). The design of these components can be varied to suit the design requirements and type of image apparatus  10 . Further, the image apparatus  10  could be designed without one or more of these components. Additionally or alternatively, the image apparatus  10  can be designed to capture a video of the scene  12 . 
     The apparatus frame  236  can be rigid and support at least some of the other components of the image apparatus  10 . In one embodiment, the apparatus frame  236  includes a generally rectangular shaped hollow body that forms a cavity that receives and retains at least some of the other components of the camera. 
     The apparatus frame  236  can include an aperture  244  and a shutter mechanism  246  that work together to control the amount of light that reaches the capturing system  240 . The shutter mechanism  246  can be activated by a shutter button  248 . The shutter mechanism  246  can include a pair of blinds (sometimes referred to as “blades”) that work in conjunction with each other to allow the light to be focused on the capturing system  240  for a certain amount of time. Alternatively, for example, the shutter mechanism  246  can be all electronic and contain no moving parts. For example, an electronic capturing system  240  can have a capture time controlled electronically to emulate the functionality of the blinds. 
     The optical assembly  238  can include a single lens or a combination of lenses that work in conjunction with each other to focus light onto the capturing system  240 . In one embodiment, the image apparatus  10  includes an autofocus assembly (not shown) including one or more lens movers that move one or more lenses of the optical assembly  238  in or out until the sharpest possible image of the subject is received by the capturing system  240 . 
     The capturing system  240  captures information for the images  14 ,  16  (illustrated in  FIG. 1 ). The design of the capturing system  240  can vary according to the type of image apparatus  10 . For a digital-type camera, the capturing system  240  includes an image sensor  250  (illustrated in phantom), a filter assembly  252  (illustrated in phantom), and a storage system  254  (illustrated in phantom). 
     The image sensor  250  receives the light that passes through the aperture  244  and converts the light into electricity. One non-exclusive example of an image sensor  250  for digital cameras is known as a charge coupled device (“CCD”). An alternative image sensor  250  that may be employed in digital cameras uses complementary metal oxide semiconductor (“CMOS”) technology. 
     The image sensor  250 , by itself, produces a grayscale image as it only keeps track of the total quantity of the light that strikes the surface of the image sensor  250 . Accordingly, in order to produce a full color image, the filter assembly  252  is generally used to capture the colors of the image. 
     The storage system  254  stores the various images before these images are ultimately printed out, deleted, transferred or downloaded to an auxiliary storage system or a printer. The storage system  254  can be fixedly or removable coupled to the apparatus frame  236 . Non-exclusive examples of suitable storage systems  254  include flash memory, a floppy disk, a hard disk, or a writeable CD or DVD. 
     The control system  24  is electrically connected to and controls the operation of the electrical components of the image apparatus  10 . The control system  24  can include one or more processors and circuits, and the control system  24  can be programmed to perform one or more of the functions described herein. In  FIG. 2 , the control system  24  is secured to the apparatus frame  236  and the rest of the components of the image apparatus  10 . Further, in this embodiment, the control system  24  is positioned within the apparatus frame  236 . 
     Referring back to  FIG. 1 , in certain embodiments, the control system  24  includes software and/or firmware that utilizes one or more methods to determine if a given image  14 ,  16  is sharp or blurred, as described herein. In various embodiments, the controller  24  includes firmware that has previously been programmed or “trained” during the manufacturing process and/or software that detects whether a new image (also sometimes referred to herein as a “test image”) subsequently taken by the image apparatus  10  is sharp or blurred. As provided herein, a line of separation between what is considered a sharp image  14  and what is considered a blurred image  16  can be adjusted to suit the requirements of the user. 
     Further, in certain embodiments, the control system  24  can include software that reduces the amount of blur  20  in a blurred image  16  to provide an adjusted image  55 . In this example, the control system  24  can determine that the first image  14  is sharp and that no further processing is necessary. Further, the control system  24  can determine that the second image  16  is blurred. Subsequently, the control system  24  reduces the blur in the second image  16  to provide the adjusted image  55 . 
     The image apparatus  10  can include an image display  56  that displays the raw images  14 ,  16  and/or the adjusted image  55 . With this design, the user can decide which images  14 ,  16 ,  55 , should be stored and which images  14 ,  16 ,  55 , should be deleted. In  FIG. 1 , the image display  56  is fixedly mounted to the rest of the image apparatus  10 . Alternatively, the image display  56  can be secured with a hinge mounting system (not shown) that enables the display  56  to be pivoted. One non-exclusive example of an image display  56  includes an LCD screen. Further, the image display  56  can display other information that can be used to control the functions of the image apparatus  10 . 
     Moreover, the image apparatus  10  can include one or more control switches  58  electrically connected to the control system  24  that allows the user to control the functions of the image apparatus  10 . For example, one or more of the control switches  58  can be used to selectively switch the image apparatus  10  to the blur evaluation and reduction processes disclosed herein. For example, in certain embodiments, in order to save computation, the present invention can be selectively applied (manually or automatically) to a certain type of image, e.g. close up portraits/statutes, macros, low light images, etc. 
       FIG. 3  is a flow chart that illustrates one embodiment of an image detection process having features of the present invention. In this embodiment, the image detection process can include a training phase (illustrated as dashed box  300 ) and a testing phase (illustrated as dashed box  302 ). The training phase  300  utilizes a set of images (also sometimes referred to herein as a “training image set”). In one embodiment, the training image set includes at least one sharp image  14  (illustrated in  FIG. 1 ) and at least one blurry image  16  (illustrated in  FIG. 1 ) which are used during the training phase  300 . The training phase  300  can include one or more of the following steps: collecting a sharp/blur classification  360  for each image in the training image, collecting image features  362 , collecting metadata features  364 , support vector machine (“SVM”) training  366 , and generating a training model (also sometimes referred to herein as a “classifier”) that determines a separation surface between sharp and blurry images  368 . As used herein, image features and metadata features are collectively referred to herein as “data features”. Each of these training phase steps is described in greater detail below. 
     During the training phase  300 , each of the images in the training image set is classified as being either sharp or blurred images at step  360 . The number of images that are classified at step  360  can vary. However, in general, a greater number of images that are classified at step  360 , and used during the training phase  300 , will increase the accuracy during the testing phase  302 . For example, hundreds or even thousands or more of each of sharp and blurry images can be used during the training phase  300 . 
     At step  360 , the images can be subjectively and/or objectively classified based on the opinion(s) of one or more persons with skills or expertise at determining sharp versus blurred images. Ideally, this classification step  360  should include a substantially consistent classification protocol of all images reviewed during the classification step  360 . 
     Next, image features  576 - 579  (illustrated in  FIG. 5A ) are collected at step  362 . The types of image features that can be collected during step  362  can vary. In order to generate the image features, each image undergoes a multi-scale image decomposition, such as a Laplacian pyramid, as one non-exclusive example, provided in greater detail below. Alternatively, other types of multi-scale image decomposition known to those skilled in the art can be utilized with the present invention, such as a steerable pyramid or a wavelet pyramid, as non-exclusive examples. Prior to the multi-scale image decomposition, each image can be converted to a grayscale image. In addition, each image can be cropped or downsized such as one-half of the original resolution, as one non-exclusive example. Further, each image can be auto-scaled to [0 255], by increasing or decreasing the contrast as needed, for example. Although it is not required that each of the foregoing steps occur prior to building the Laplacian pyramid, the training process can be enhanced in this manner. 
     In  FIGS. 4A-4C , a six level Laplacian pyramid  408  (illustrated in  FIG. 4C ) is described. Alternatively, it is recognized that in embodiments where a Laplacian pyramid-type decomposition is utilized, greater or fewer than six levels can be generated depending upon the design requirements of the system and/or process. 
     Referring to  FIG. 4A , the generating the first level of the Laplacian pyramid  408  is described. An original image (I) that was previously classified in step  360  is subjected to a low pass filter to artificially blur the original image (I) into a blurred image B 0 , at step  404 . This blurred image B 0  is then downsampled at step  405 , typically by one-half in each dimension, into image B 1 . 
     At step  406 , downsampled image B 1  is then upsampled to the same size as image B 0  and the original image (I), to generate image B 1U . At step  407 , image B 1U  is subtracted from the original image (I) to generate the first level of the Laplacian pyramid  408 , as represented by image L 0  in  FIG. 4A . 
       FIG. 4B  illustrates how the remainder of the Laplacian pyramid  408  is generated for a six-level pyramid. Image B 1  is subjected to a low pass filter and downsampled into image B 2  in a manner substantially similar to that described previously herein. Image B 2  is then upsampled to the same size as image B 1  to generate image B 2U . Image B 2U  is subtracted from image B 1  to generate the second level of the Laplacian pyramid  408 , as represented by image L 1  in  FIG. 4B . In a somewhat similar manner, certain successive levels of the Laplacian pyramid  408  is generated as illustrated in  FIG. 4B , to generate images L 2  through L 4 . For level L 5 , image B 5  is used as is. 
       FIG. 4C  illustrates a representation of Laplacian images L 0  through L 5 , which form the Laplacian pyramid  408 . 
       FIG. 5A  is a flow chart illustrating one embodiment of the methodology for collecting image features as previously indicated at step  362  in  FIG. 3 . In the embodiment in  FIG. 5A , for each level of the Laplacian pyramid  408  (described relative to  FIGS. 4A-4C ), one or more statistical moments are measured at step  576 . In one embodiment, the four-order statistical moments are measured, including a mean, a standard deviation, a kurtosis and a skewness, for a total of 24 image features. Alternatively, greater or fewer than 24 image features generated from the statistical moments can be utilized. 
     Alternatively, or additionally, at step  577 , for one or more pairs of neighboring levels of the Laplacian pyramid, a covariance is determined. For example, in one embodiment, the covariance is computed between levels L 0  and L 1 , L 1  and L 2 , L 2  and L 3 , and L 3  and L 4 , for a total of four additionally image features. Alternatively, the covariance can be omitted for any of these neighboring pairs. 
     At step  578 , a scaling factor can be applied to adjust the intensity of the original image to the [0 1] range, to provide another image feature. 
     At step  579 , the four-order statistical moments (mean, standard deviation, kurtosis and skewness) of the intensity of the image are determined, for a total of four additional image features. 
     It should be noted that one or more of the steps  576 - 579  can be omitted without deviating from the spirit of the present invention, although to increase accuracy, each of the steps  576 - 579  is advisable. 
     Referring back to  FIG. 3 , the training process  300  further includes collecting metadata features at step  364 . 
       FIG. 5B  is a flow chart illustrating one embodiment of the methodology for collecting metadata features as previously described at step  364  in  FIG. 3 . In the embodiment in  FIG. 5B , one or more of the metadata features ( 580 - 586 ) can be collected for the original image, which can include focal length  580 , f-number  581 , exposure time  582 , ISO sensitivity  583 , whether or not a flash was used to take the original image  584 , an exposure value  585  and/or a handholdable factor (X)  586 . Each of these metadata features is a standard feature for a typical image apparatus  10  such as a digital camera. In an alternative embodiment, the metadata features  580 - 586  are not collected and are not utilized during the training process  300  (illustrated in  FIG. 3 ). 
     Referring again to  FIG. 3 , at step  366 , a non-linear SVM training occurs utilizing the data features (image features and metadata features) collected from all of the images in the training image set. In general, the SVM is a relatively new generation machine learning technique based on the statistical learning theory. 
       FIG. 5C  is a flow chart illustrating one embodiment of the methodology for the SVM training based on the data features collected from the training image set. In the embodiment in  FIG. 5C , the data features, including the image features  576 - 579  and the metadata features  580 - 586 , are normalized to the [0 1] range or another suitable range, at step  587 . In so doing, any bias toward any one particular data feature is reduced or avoided. These normalized data features are also referred to herein as a “training statistical signature”. 
     At step  588 , in one embodiment, all normalized data features are randomly split into either training or testing parts. For example, the training image set and/or the normalized data features from the training image set can be split by some percentage, e.g., 80% into training and 20% into testing. In one embodiment, the split is by random selection. In this example, 80% of the training data features is used to construct the model(s) (step  590  below). The model is then used for testing of the remaining 20% of the data features. In this embodiment, less than 100% of the data features for training is used in order to avoid over-fitting of data. The model that can still give adequate or better performance will be favorable, because this model can generalize to new images. In an alternative embodiment, all 100% of the data features are used to train the model. However, in this embodiment, although the model may perform adequately or better on these data features, this model may have a higher likelihood of over-fitting. 
     At step  589 , a radial basis function (RBF) kernel is used as the basis for the SVM. For example, the RBF kernel is represented by the equation:
 
 K ( x,y ) =e   −γ∥x-y∥     2     [1]
 
     wherein the kernel is a defined inner product in mapped high dimensional space. 
     At step  590 , one or more models are generated that are based on parameter grids of (C, γ). 
     At step  591 , cross-validation can be utilized to find the desired model. Additionally, or in the alternative, the amount of support vectors can be constrained to find the desired model. The desired model is selected from a plurality of models that have been generated in accordance with the previous steps herein. The model to be utilized in the testing phase  302  (illustrated in  FIG. 3 ) can depend upon various factors. In one embodiment, these factors can include one or more of the type of blur being detected (motion vs. defocus), the type/quality of camera that will generate images being tested during testing phase  302 , compression level of images (none, fine, coarse, etc.), denoising level, anticipated requirements of the user, etc. Alternatively, if only one model was generated, that model is then utilized for the testing phase  302 . 
     Referring back to  FIG. 3 , the testing phase  302  includes one or more of collecting image features from a test image (step  370 ), collecting metadata features from the test image (step  372 ), and SVM prediction (step  374 ). At step  370 , essentially the same process is used for the test image that was used during the training phase for collection of image features. Stated another way, the same types of image features are collected in substantially the same manner that was followed for step  362 , except a test image is used. In one embodiment, different classifiers are generated to separate sharp from motion blur or to separate sharp from defocus blur. In another embodiment, different classifiers are generated to compensate for the difference of compression levels, denoising levels, etc. 
     Additionally, at step  372 , essentially the same process is used for the test image that was used during the training phase for collection of metadata features. Stated another way, the same types of metadata features are collected in substantially the same manner that was followed for step  364 , except a test image is used. 
       FIG. 5D  is a flow chart illustrating one embodiment of the methodology for the SVM prediction of sharp or blur for the test image as illustrated in step  374  in  FIG. 3 . Step  592  in  FIG. 5D  also summarizes previously described steps  370  and  372  for the sake of continuity. The collected data features from step  592  are normalized to the range [0 1], or another suitable range at step  593 , using the same mapping function from the SVM training phase  300  (illustrated in  FIG. 3 ) at step  594 . The normalized data features from the test image are also sometimes referred to herein as a “testing statistical signature”. 
     At step  595 , a prediction of whether the test image is sharp or blurry is made based on the model from the training phase  300 . A weighted sum of kernel function is computed from a data feature vector to support vectors by the following equation: 
     
       
         
           
             
               
                 
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     where the SVM model structures include an RBF kernel type; γ is a model parameter (for RBF kernel function); x i  is a supporting vector; x is a data feature from the test image; α i  is a corresponding weight; y i  is a classification label; and b is a bias term. Based on a whether the value from the testing phase  302  which utilizes the model selected during the training phase  300  is positive or negative, a prediction and/or determination can be made for whether the test image is sharp or blurry. 
       FIG. 6  is a flow chart that illustrates another embodiment of an image detection process having features of the present invention. In this embodiment, the image detection process includes a training phase (illustrated as dashed box  600 ) and a testing phase (illustrated as dashed box  602 ). In essence, the image detection process illustrated in  FIG. 6  has certain similarities to that previously described relative to  FIG. 3 , with the following exceptions described in greater detail below. 
     The training phase  600  can include one or more of the following steps: collecting a sharp/blur classification  660  for each image in the training image, collecting image features  663 , collecting metadata features  664 , linear discrimination analysis (“LDA”) training  665 , and generating a training model that determines a threshold between sharp and blurry images  668 . In this embodiment, the training is based on a linear analysis at step  665 , rather than the non-linear analysis (SVM) described previously herein. Thus, the sharp/blur classification (step  660 ) and metadata feature collection (step  664 ) are substantially similar to those described previously herein. 
       FIG. 7A  is a flow chart that describes one embodiment for collection of image features that occurs during step  663  in the embodiment illustrated in  FIG. 6 . In this embodiment, the collection of image features includes measuring the four-order statistical moments, including a mean, a standard deviation, a kurtosis and a skewness, at step  776 . In one embodiment, these statistical moments can be measured for one or more, or all levels of the Laplacian pyramid. 
     Alternatively, or additionally, at step  777 , for one or more pairs of neighboring levels of the Laplacian pyramid, a covariance is determined in a manner substantially similar to that described previously herein. 
     At step  779 , the four-order statistical moments (mean, standard deviation, kurtosis and skewness) of the intensity of the image are determined, for a total of four additional image features. 
       FIG. 7B  is a flow chart that describes one embodiment for LDA training that occurs during step  665  in the embodiment illustrated in  FIG. 6 . In the embodiment in  FIG. 7B , the data features and the metadata features are normalized to the [0 1] range or another suitable range, at step  787 . In so doing, any bias toward any one particular data feature is reduced or avoided. 
     At step  788 , in one embodiment, all normalized data features are randomly split into either training or testing parts in a manner that is substantially similar to that previously described herein. 
     At step  789 , the data features are selected to ultimately increase the accuracy of the training model. In one embodiment, less than the total number of data features is used to generate the training model. Alternatively, all of the data features can be used. In the embodiment that uses fewer than the total number of data features, the method for selecting the data features can vary. In one embodiment, the data features can be selected one-at-a-time, starting with what is deemed to be the most important data feature (or one of the more important data features). Each of the remaining data features is then tested with this first data feature to determine which of the remaining data features provides the best performance and/or accuracy in conjunction with the first data feature. This process continues until the desired number of data features has been used. Alternatively, any suitable method for selecting data features can be utilized without deviating from the scope of the invention. 
     At step  790 , in one embodiment, one model is selected that provides the least difference between sharp and blur accuracy. In this embodiment, each model is tested for accuracy for detecting both sharp images and blurred images. Once all models have been tested, the appropriate model is selected. For example, a first model may provide 92% sharp accuracy and 80% blur accuracy, while a second model may provide 90% sharp accuracy and 86% blur accuracy. Using this example, in one embodiment, the second model would be preferred because the range of accuracy between detecting sharp and blurred images is narrower. In various embodiments, the model selection criteria are application dependent. For instance, the application may require a higher sharp rate than blurry rate, so the best-fit model can be selected accordingly. In an alternative embodiment, another method can be utilized for selecting the appropriate model. 
     Referring back to  FIG. 6 , at step  668 , the model can provide a threshold value for a sharp image or a blurred image, as described in greater detail below. 
     At step  670 , essentially the same process is used for the test image that was used during the training phase  600  for collection of image features. Stated another way, the same types of image features are collected in substantially the same manner that was followed for step  663 , except a test image is used. 
     Additionally, at step  672 , essentially the same process is used for the test image that was used during the training phase  600  for collection of metadata features. Stated another way, the same types of metadata features are collected in substantially the same manner that was followed for step  664 , except a test image is used. 
       FIG. 7C  is a flow chart that describes one embodiment for LDA prediction that occurs during the testing phase  602  at step  673  in the embodiment illustrated in  FIG. 6 . Step  792  in  FIG. 7C  also summarizes previously described steps  670  and  673  for the sake of continuity. The collected data features from step  792  are normalized to the range [0 1], or another suitable range at step  793 , using the same mapping function from the LDA training phase  600  (illustrated in  FIG. 6 ) at step  794 . In one embodiment, the data features collected from the test image during the testing phase  602  are the same or substantially similar to those collected (and selected) during the training phase  600 . 
     At step  795 , a classifier predicts and/or determines whether the test image is sharp or blurry. In one embodiment, a weighted sum of the normalized data features is compared to the threshold value of the training model. Depending upon whether this threshold value is met or exceeded by the weighted sum value of the test image, the classifier can predict a sharp or blurry image for the test image. For example, in one embodiment, if the weighted sum value of the test image is less than the threshold value of the training model, and the center of projected blur image features is also less than the threshold of the training model, the image can be predicted to be blurry. Conversely, if the weighted sum value of the test image equal to or greater than the threshold value of the training model, and the center of projected sharp image features is also greater than the threshold of the training model, the image can be predicted to be sharp. 
     In another embodiment, the model can provide various ranges of values that can predict, to a desired confidence level, the extent of the blur in a test image. For example, if the test image value falls within a particular range of values, the test image can be categorized as “slightly blurred”, rather than “blurred”. This may be helpful to the user since a slightly blurred image may be worthy of further image manipulation to correct the blur, while a blurred image may not be correctable, and can be deleted to free up memory space and/or avoid printing time and resources. 
     While the current invention is disclosed in detail herein, it is to be understood that it is merely illustrative of the presently preferred embodiments of the invention and that no limitations are intended to the details of construction or design herein shown other than as described in the appended claims.