1. Field of the Invention
The present invention relates to image compression technology. More particularly, the present invention relates to a method for filtering image noise using pattern information. Most particularly, the present invention relates to an image noise filtering method which can effectively filter image noise remaining on an edge of an image before or after the image is compressed in a display unit, thereby substantially producing images in which noise is filtered.
2. Description of the Related Art
Generally, a charge coupled device (CCD) or CMOS sensor has the characteristic of generating noise due to a quantity of light or heat. Such noise does not have a Gaussian (or Laplacian) statistic characteristic but has a signal-dependent characteristic. Therefore, since the noise of the sensor is not white, it is difficult to easily filter the noise by a general noise filtering algorithm. Further, although the noise is filtered, a segment of a high frequency of the signal can be damaged by the filtering. Noise filtering is one of image processing fields which have been researched for a long time. Such a noise filtering algorithm may be generally classified into a technology using a restoration concept, and a method using a filtering technology.
Since the restoration technology is based an accurately modeling the noise, it is possible to obtain an excellent result. However, the restoration technology is computationally intensive. Thus, a method using a statistical characteristic of a local region of an image, for example a Local Linear Minimum Mean Square Error (LLMMSE), is often used.
On the other hand, filtering technologies which can be realized by means of hardware have also been used for image processing fields. Mean series filters have been used in order to filter noise having Gaussian statistical characteristics, while median series filters have been used in order to filter noise having Laplacian statistical characteristics.
In the mean series filtering method, a mean filter and a median filter are used to filter image noise. The method for filtering the image noise using the mean filter is a basic mean filter, which calculates a mean of the value of inner pixels in a local region of the image. This is low pass filtering and has a disadvantage of filtering a segment of a high frequency necessary for the image as well as noise, resulting in filtering precise portions of the image. In order to solve the above-mentioned problem, a local statistic of an image is calculated using equations (1) and (2), reflecting a non-stationary characteristic of the image, under a condition that a contour is not crossed in a mask:
                                                        x              ^                                      AWA              ⁡                              (                                  m                  ,                                      n                    ;                    k                                                  )                                              =                                    ∑                              i                ,                                  j                  ∈                                      S                                          m                      ,                                              n                        ;                        k                                                                                                                  ⁢                                          w                ⁡                                  (                                      i                    ,                                          j                      ;                      l                                                        )                                            ⁢                              y                ⁡                                  (                                      i                    ,                                          j                      ;                      l                                                        )                                                                    ,                            Equation        ⁢                                  ⁢                  (          1          )                    wherein Sm,n,k is a mask.
                                          w            ⁡                          (                              i                ,                                  j                  ;                  l                                            )                                =                                    k              ⁡                              (                                  m                  ,                                      n                    ;                    k                                                  )                                                    1              +                              a                ⁡                                  (                                      max                    ⁡                                          [                                                                        ɛ                          2                                                ,                                                                              (                                                                                          g                                ⁡                                                                  (                                                                      m                                    ,                                                                          n                                      ;                                      k                                                                                                        )                                                                                            -                                                              g                                ⁡                                                                  (                                                                      i                                    ,                                                                          j                                      ;                                      l                                                                                                        )                                                                                                                      )                                                    2                                                                    ]                                                        )                                                                    ,                            Equation        ⁢                                  ⁢                  (          2          )                    wherein k(m,n;k) is a normalization constant.
The image noise filtering method using the median filter effectively filters the Laplacian noise (first statistical noise characteristic) such as salt and pepper noise. The filtering method is calculated using the equation (3):{circumflex over (x)}MF(m,n)=median {y(i,j)|(i,j∈Sm,n)}  Equation (3)
The median filter effectively filters noise in an even region excluding an edge. However, the median filter has a disadvantage of damaging information along a narrow line or corner.
Further, a conventional LLMMSE filter is based on a Non-stationary Mean Non-stationary Variance (NMNV) image model, and is expressed using equation (4):
                                                        x              ^                                      LLMMSE              ⁡                              (                                  i                  ,                  j                                )                                              =                                    E              ⁡                              (                                  y                  ⁡                                      (                                          i                      ,                      j                                        )                                                  )                                      +                                                                                σ                    x                    2                                    ⁡                                      (                                          i                      ,                      j                                        )                                                                                                              σ                      x                      2                                        ⁡                                          (                                              i                        ,                        j                                            )                                                        +                                                            σ                      n                      2                                        ⁡                                          (                                              i                        ,                        j                                            )                                                                                  ⁢                              (                                                      y                    ⁡                                          (                                              i                        ,                        j                                            )                                                        -                                      E                    ⁡                                          (                                              y                        ⁡                                                  (                                                      i                            ,                            j                                                    )                                                                    )                                                                      )                                                    ,                            Equation        ⁢                                  ⁢                  (          4          )                    
where and σx2 and σn2 indicate a non-stationary dispersion of x and n, respectively. Here, it is assumed that the noise dispersion σn2 is a value which is presumed or already known. It is well known that the LLMMSE filter smoothes the noise in the even region (σx2≅σn2)({circumflex over (x)}LLMMSE(i, j)≅E(y(i, j))), but does not filter small pixels (σx2>>σx2) near a boundary, as shown in FIG. 1 ({circumflex over (x)}LLMMSE(i, j)≅y(i, j)).
A mean value series filter is easily calculated so that it can be realized in real-time. However, since a weight function used for preserving contour is determined by a difference between two pixel values, the mean value series filter is affected by the noise and cannot effectively filter the noise around the contour. Further, the mean value series filter has a disadvantage in that the images are contrived because of too much smoothing of the noise in the even region.
The median series filter is rarely used because of the difficulty of realizing it in hardware due to it computation intensity. The LLMMSE filter can effectively filter noise in the even region, but cannot effectively filter noise in a region near the boundary. Further, the LLMMSE filter has a disadvantage in that the weight function is affected by the noise, like the mean series filter.