Patent Publication Number: US-8111943-B2

Title: Smart image enhancement process

Description:
ORIGIN OF THE INVENTION 
     This invention was made in part by employees of the United States Government and may be manufactured and used by or for the Government of the United States of America for governmental purposes without the payment of any royalties thereon or therefor. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to digital image processing. More specifically, the invention is a method for optimizing the visual quality of any digital image based on contrast, lightness and sharpness measures thereof. 
     2. Description of the Related Art 
     The image of a scene captured by imaging equipment is affected by the environments between the imaging equipment and the scene. For example, if the environment is a low-light environment, image features can be lost due to flow contrast and low lightness. If the environment is turbid (e.g., foggy, smoke, rain, snow, murky water, etc.), there is very little contrast in an image. The combination or low light and a turbid environment makes image feature detection even more difficult. 
     Conventional image processing approaches are typically designed to cope with one of these environments but not the effects caused by combinations of these environments. Further, conventional image processing approaches are either manual methods or passive automatic image enhancement methods that do not evaluate and adapt to visual qualities. The manual methods require significant operator training, are time consuming and expensive, and/or are inconvenient for some applications. Existing automatic methods include auto level enhancement, histogram enhancement, and retinex image processing as disclosed in U.S. Pat. Nos. 5,991,456, 6,834,125 and 6,842,543. 
     Auto level or “fixed gain” enhancement does not work with wide dynamic range images as saturation occurs. Histogram enhancement performance is unpredictable. Retinex image processing performs relatively well in terms of contrast and lightness enhancement across wide ranging imaging conditions. However, the effectiveness of retinex image processing is reduced for narrow dynamic range images generated in low-light or turbid environments. Finally, each of the automatic enhancement approaches operates on all images even when some images are visually acceptable. From a processing cost perspective, this is inefficient. 
     SUMMARY OF THE INVENTION 
     Accordingly, it is an object of the present invention to provide a method of image processing that effectively and efficiently enhances images that are unsatisfactory. 
     Another object of the present invention is to provide an automatic method of image enhancement. 
     Still another object of the present invention is to provide an image enhancement method that achieves pattern constancy for a variety of low light, low-contrast, and/or turbid imaging environments. 
     Other objects and advantages of the present invention will become more obvious hereinafter in the specification and drawings. 
     In accordance with the present invention, a method of smart image processing is provided. Contrast and lightness measures are computed for a digital image and used to first classify the image as being one of non-turbid and turbid. If a turbid image, the original image is enhanced to generate a first enhanced image. If a non-turbid image, the original image is then classified as having one of a good contrast/lightness score and a poor contrast/lightness score based on the contrast and lightness measures. The non-turbid image is enhanced when a poor contrast/lightness score is associated therewith. As a result, a second enhanced image is generated. A revised contrast measure and revised lightness measure are computed for the second enhanced image. This second enhanced image is then classified as having one of a good contrast/lightness score and a poor contrast/lightness score based on the revised contrast and lightness measures. When the second enhanced image has a poor contrast/lightness score associated therewith, this image is enhanced so that a third enhanced image is generated. A sharpness measure is computed for one image that is selected from (i) the non-turbid image, the first enhanced image, (iii) the second enhanced image when a good contrast/lightness score is associated therewith, and (iv) the third enhanced image. This selected image is then classified as having one of a sharp image score and a not-sharp image score based on the sharpness measure. The selected image having a not-sharp score associated therewith is then sharpened to generate a sharpened image. A contrast measure and revised sharpness measure are then computed for the sharpened image. The sharpened image is classified as having one of a sharp image score and a not-sharp image score based on the contrast measure associated with the sharpened image. The final image is selected from (i) the selected image having the sharp image score, (ii) the sharpened image having the sharp image score, and, in some instances, (iii) the sharpened image having the not-sharp image score. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow diagram of an image processing method in accordance with the present invention; 
         FIG. 2  is a flow diagram of turbid image detection and enhancement processing in accordance with an embodiment of the present invention; 
         FIG. 3  is a flow diagram of an embodiment of image enhancement for a low-contrast imaging environment, and 
         FIG. 4  is a flow diagram of an embodiment of image enhancement for a low-contrast and low-light imaging environment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring now to the drawings and more particularly to  FIG. 1 , a flow diagram of an image processing method in accordance with the present invention is shown. The method operates on digital image data that can be captured by digital imaging equipment, or analog imaging equipment coupled to an analog-to-digital converter. Accordingly, the choice of imaging equipment is not a limitation of the present invention. In addition, the digital image data processed in accordance with the present invention can be a single still image or a single frame from a video data stream. Still further, the method described herein can be applied as post-processing to archived image data, or can be incorporated into imaging equipment to provide real-time image (i.e., still or video image) enhancement. 
     In accordance with the present invention, a raw incoming image  100  in digital form will be provided. Image  100  is a single frame of an image and is defined by an N×M array of pixels with each pixel having an intensity value associated therewith as would be well understood in the art. As mentioned above, image  100  can be a still image or a single frame from a video stream as processing will be the same in either case. In general, image  100  will be evaluated in accordance with a number of novel “measures” of visual quality, and then enhanced (if necessary) predicated on the computed measures. The process is automatic and adapts to all imaging environments. Thus, the present invention can be viewed as a “visual servo control” process. 
     The first measures are computed for image  100  at step  102 . These first measures are a contrast measure “C” and lightness measure “L”. While the determination of these measures will be described further below, it is sufficient at this point in the description to say that these two measures define the contrast and lightness of image  100  relative to predetermined/acceptable criteria. 
     Image  100  is then evaluated in terms of its turbidity at step  104 . In general and as referred to herein, a “turbid” image is one exhibiting low contrast due to (i) image environment conditions such as fog, haze, smoke, rain, snow, cloudy or muddy water, etc., that cloud the medium between the scene and the imaging device, (ii) insufficient light at the time of image capture as is the case during the low-light times of dawn or dusk, or (iii) severe underexposure errors during image acquisition. 
     Step  104  utilizes the computations of step  102  to perform one or more evaluations of image  100  to determine if image  100  is a turbid image. If image  100  is determined to be turbid, the image is enhanced at step  106 . Details of turbid-image determination step  104  and an exemplary enhancement process  106  will be described in detail further below. If generated, the enhanced turbid image is supplied to a sharpness measure computation step  118 . 
     If image  100  is not turbid, processing of image  100  proceeds to step  108  where a merged contrast and lightness classification is performed using contrast measure C and lightness measure L. At this point in the description, it is sufficient to say that step  108  classifies image  100  as being either GOOD or POOR in terms of the present invention&#39;s merged contrast/lightness evaluation that will be described further below. If image  100  is classified as GOOD, image  100  is provided to sharpness measure computation step  118 . 
     If image  100  is classified as being POOR in terms of its contrast/lightness evaluation, then image  100  is enhanced at step  110 . For example, enhancement step  110  can utilize retinex processing techniques disclosed by one or more of U.S. Pat. Nos. 5,991,456, 6,334,125 and 6,842,543, the contents of which are each hereby incorporated by reference in their entirety. Should enhancement step  110  utilize the processing techniques disclosed by all three of these patents, enhancement step  110  is said to employ a “multi-scale retinex with color restoration” (MSRCR) process as it is known in the art. However, it is also to be understood that step  110  is not limited to the MSRCR process as other or additional image enhancement techniques could be used without departing from the scope of the present invention. 
     The resulting enhanced image from step  110  is re-evaluated in terms of contrast and lightness. More specifically, the enhanced image from step  110  has contrast and lightness measures associated therewith computed at step  112  where such computations are the same ones used in step  102 . The computed contrast measure “C E ” and lightness measure “L E ” for the enhanced image are then utilized in a merged fashion by classification step  114 . Classification determination processing at step  114  is the same as that performed at step  108 . As a result of step  114 , the enhanced image from step  110  is classified as being either GOOD or POOR in terms of the present invention&#39;s merged contrast/lightness evaluation. 
     If step  114  classifies the enhanced image as POOR, the enhanced image (from step  110 ) is further enhanced (e.g., by auto level processing, histogram modification, etc.) at step  116 . One of the enhanced image classified as GOOD or the re-enhanced image from step  116  is provided to a sharpness measure computation step  118 . 
     As a result of the above-described processing, one “image” is provided to step  118  for computation of a sharpness measure associated therewith. The image provided to step  118  can be the original image  100  (i.e., a GOOD classification from step  108 ), an enhanced turbid image from step  106 , an enhanced original image classified as GOOD at step  114 , or a re-enhanced image from step  116 . Regardless of the “image” provided thereto, step  118  generates a sharpness measure therefrom and provides same to a classification step  120  that evaluates the sharpness of the currently-processed image in terms of it sharpness. Once again, while details of step  120  will be provided further below, it is sufficient at this point to say that step  120  identifies the currently-processed image as SHARP or NOT SHARP. 
     An image classified as SHARP becomes an outgoing image  200  requiring no additional processing. An image classified as NOT SHARP is sharpened at step  122  in accordance with any one or more image sharpening techniques, a variety of which are well known in the art. For reasons that will become clearer below, the sharpened image from step  122  has a contrast measure associated therewith computed at step  124 . This computation is the same as that used in steps  102  and  112 . The sharpened image is provided to step  124  where a new contrast measure is computed. This computation is the same as that used in steps  102  and  112 . Classification step  120  is then repeated using the new sharpness measure (step  118 ) and contrast measure (step  124 ) computed for the sharpened image. 
     When testing the present invention, it was discovered that the sharpening “loop” does not provide image improvements after a few passes. Accordingly, classification step  120  can include a counter operation to limit the number of passes therethrough thereby preventing “infinite loop” processing. In this case, outgoing image  200  could also be defined by a sharpened image that is still classified as NOT SHARP by the criteria embodied in step  120 . By making classification step  120  a “count-limited” classification step, the processing method will be guaranteed to generate outgoing image  200  with efficiency. 
     The various “measure” computations and turbid image detection/processing will now be described. It will be assumed that the image being evaluated has multiple spectral channels (e.g., colors, bands, etc.). Contrast and lightness measures are determined in the following fashion. The image being evaluated is divided evenly into “R” non-overlapping blocks or regions. For each j-th spectral channel of each k-th region, the mean and standard deviation are determined. Then, the maximum spectral mean and maximum spectral standard deviation are selected for further processing. That is, for each k-th region, the mean μ k  selected for further processing is
 
μ k =max(μ j ),  j= 1 , . . . ,J   (1)
 
and the standard deviation σ k  selected for further processing is
 
σ k =max(σ j ),  j= 1 , . . . ,J   (2)
 
where J is the number of spectral channels. Thus, μ k  and σ k  are indicative of perceived lightness and contrast, respectively, of the image.
 
     The next step in determining the contrast measure C is to classify each k-th region as having good or poor contrast. A region&#39;s contrast is good when
 
σ k   ≧K   1   (3)
 
     where K 1  is a predetermined canonical value. The contrast measure C is the number of regions having good contrast divided by the total number of regions R. The first step in determining the lightness measure L is to determine which regions having poor contrast have good lightness. A region&#39;s lightness is good when
 
μ k   ≧K   2   (4)
 
where K 2  is a predetermined threshold value. The lightness measure L is the number of regions satisfying equation (4) divided by the total number of regions R.
 
     Contrast and lightness classification steps  108  and  114  perform a merged contrast/lightness classification in accordance with the following logic:
 
If  C≧K   3  AND ( C+L )≧ K   4 ,  (5)
 
     then classify the image as having GOOD lightness and contrast; else, classify the image having POOR lightness and contrast. 
     Here, the constants K 3  and K 4  are predetermined via experimentation. 
     The sharpness measure computation begins by convolving the image (provided to step  118 ) with a smallest Difference-of-Gaussian kernel in accordance with methods disclosed by D. Jobson in “Spatial Vision Procession: From the Optical Image to the Symbolic Structures of Contour Information,” NASA Technical Paper No. 2838, November, 1988, and F. Huck et al. in “Visual Communication: An Information Theory Approach,” Kluwer Academic Publishers, 1997, p. 145. The resulting matrix of image pixels S(x,y) is then half-rectified to identify a matrix S′ (x,y) of all non-negative-intensity-value pixels or
 
 S ′( x,y )= S ( x,y )≧0  (6)
 
     Next, each non-negative-intensity-value pixel S′(x,y) is classified as SHARP or NOT SHARP in accordance with the following relationship:
 
If  S ′( x,y )≧ K   5 ,  (7)
 
     then classify the pixel at (x,y) as SHARP; else, classify the pixel at (x,y) as UNSHARP. 
     Here, the constant K 5  is an experimentally determined canonical constant. The total number of pixels classified as SHARP for an image is counted and is designated herein as “S p ”. The raw global sharpness measure “S” is the number of sharp pixels of S p  divided by the total number of pixels in the image. 
     Sharpness classification step  120  uses the sharpness measure S to classify an entire image as SHARP or NOT SHARP in accordance with the logic
 
If  S≧T,   (8)
 
     then classify the image SHARP; else, classify the image as NOT SHARP. 
     Here, T is an experimentally determined threshold of the form
 
 T= ( C/C   T ) K   6   (9)
 
where C is the contrast measure (from step  102 , step  112 , or step  124 ), C T  is a canonical constant determined For minimum GOOD contrast scoring images, and K 6  is an experimentally-determined constant.
 
     Turbid image detection and processing in accordance with the present invention will now be described with reference to  FIGS. 2-4 . Referring first to  FIG. 2 , the original image  100  is evaluated at step  1040  to determine if very low contrast conditions exist across the entire image. Such conditions are indicative of a foggy or smoky environment. More specifically, step  1040  compares the sum of the regional standard deviations μ K  (from equation (1)) to a predetermined threshold value K 7  according to the following relationship 
     
       
         
           
             
               
                 
                   
                     
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     then the original image is FOGGY; else, the image is NOT FOGGY. 
     If the original image  100  is FOGGY, it is enhanced in step  1042 . For example, step  1042  can be realized by the process illustrated in  FIG. 3  where the original image  100  is first processed at step  1042 A in accordance with a modified retinex process. That is, the modified retinex process is deadened by the processing techniques disclosed in just two of the previously-cited U.S. patents (i.e., U.S. Pat. Nos. 5,991,456 and 6,834,125). Thus, step  1042 A can be said to employ an MSRCR process with the white balancing operation (i.e., disclosed in U.S. Pat. No. 6,842,543) turned off. In foggy images, the brightness is high. Therefore, the original foggy image tends to overwhelm the white balance operation in a full MSRCR process. To counteract this, the white balance operation is turned off in order to allow the retinex processing to impact the original image. The enhanced FOGGY image is further processed by a conventional histogram modification at step  1042 B. 
     Referring again to  FIG. 2 , if the original image  100  is determined to be NOT FOGGY, it is then checked at step  1044  for the combination of low contrast and low light conditions that would typically exist at either dawn or dusk. Note that dawn and dusk are the times of day when humans experience the greatest visibility deficiency due to the prevalence of blue light. Specifically, step  1044  performs the following logic using the contrast C and lightness measure L from step  102  as follows:
 
If ( C&lt;K   9 ) AND ( L&lt;K   10 ),  (11)
 
     then a DAWN/DUSK condition exists; else, NOT DAWN/DUSK. Here, K 9  is a preset contrast measure threshold and K 10  is a preset lightness measure threshold indicative of dawn/dusk conditions. 
     If a DAWN/DUSK condition is indicated, the original image  100  is enhanced at step  1046 . For example, step  1046  can be realized by the process illustrated in  FIG. 4  where the original image  100  first has its pixel intensity values inverted at step  1046 A. The inverted-value image is then enhanced at step  1046 B by an MSRCR process with the white balancing operations turned off, i.e., the same as process step  1042 A. The pixel intensity values are inverted prior to MSRCR processing to allow the log operator (in the MSRCR process) to accentuate the details in the bright regions of the image. The enhanced DAWN/DUSK image is further processed by a conventional histogram modification at step  1046 C. 
     Referring again to  FIG. 2 , if the original image  100  is determined to be not DAWN/DUSK, it is then checked at step  1048  for the combination of low contrast with light conditions that are bright enough to not trigger DAWN/DUSK, but too dark for adequate handling by enhancement step  1042 . This condition would typically exist when there is very heavy fog or haze in daylight. Specifically, step  1048  performs the following logic using the contrast measure C and lightness measure L from step  102  as follows:
 
If ( C&lt;K   9 ) AND ( L≦K   11 ),  (12)
 
     then VERY HEAVY FOG condition exists; else, NOT VERY HEAVY FOG. 
     Here, K 11  is a preset lightness measure value satisfying K 10 &lt;L≦K 11 . 
     If the original image  100  is VERY HEAVY FOG, it is enhanced at step  1050  which can be realized by the same “MSRCR-with-white-balancing-turned-off” process described above, followed by a conventional histogram modification (i.e., the same enhancement combination as process steps  1042 A and  1042 B). However, in this case, a different set of canonical gain and offset values is used with the “MSRCR-with-white-balancing-turned-off” process to compensate for the additional poor lightness and contrast. If a NOT VERY HEAVY FOG condition exists, the original image  100  is passed to step  108  for processing as described earlier herein. If any of the turbid image detection/processing produces an enhanced image, that enhanced image is provided to sharpness measure computation step  118 . 
     The advantages of the present invention are numerous. A wide variety of image environment conditions are evaluated with the optimum image enhancement processes) being selected/implemented to optimize image contrast, lightness and sharpness. The process provides for variations in visual preferences by selection of threshold constants used throughout the process. Tests of the present invention on a wide variety of imaging conditions have yielded pattern constancy across the various conditions. In terms of image processing systems/methods, “pattern constancy” refers to a system/method&#39;s ability to extract a pattern from the image of a scene that is stable over wide ranging extraneous variations in scene lighting conditions, atmospheric turbidity, and exposure errors present in the image acquisition device. Thus, the present invention would be particularly useful in aviation to provide a pilot with (i) good and consistent images regardless of the visibility conditions, and (ii) stable pattern information for use in in-flight computer pattern processing and pattern recognition systems. 
     Although the invention has been described relative to a specific embodiment thereof, there are numerous variations and modifications that will be readily apparent to those skilled in the art in light of the above teachings. While the present invention provides an automatic “poor” image detection/enhancement process, aspects of the present invention could be used by themselves. For example, the novel contrast, lightness and/or sharpness measures could be utilized in other image classification/processing schemes. The turbid image detection and/or enhancement schemes could be used in a “stand alone” fashion. For example, the FOGGY/NOT FOGGY detection scheme could be used for aviation and underwater imaging to provide a warning or announcement that poor visibility conditions are approaching. It is therefore to be understood that, within the scope of the appended claims, the invention may be practiced other than as specifically described.