Image analysis – Image enhancement or restoration – Image filter
Reexamination Certificate
2000-06-26
2003-10-14
Mehta, Bhavesh M. (Department: 2625)
Image analysis
Image enhancement or restoration
Image filter
C382S261000, C382S264000, C382S265000, C345S611000, C345S618000, C345S631000, C348S466000, C348S606000, C358S452000, C358S463000
Reexamination Certificate
active
06633683
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to the field of image noise reducing, and more particularly to apparatus and method for adaptively reducing noise in a noisy input image or a sequence of images, wherein the noise is of an additive Gaussian type.
DESCRIPTION OF THE PRIOR ART
While existing prior patents and other publications on noise reducing techniques are abundant, they generally concern coring techniques applied in high frequency part of a considered image. Image de-noising techniques can be classified as spatial or temporal ones. Of course, a series combination of spatial and temporal techniques is possible and generally beneficial.
Temporal filters are generally applied for a sequence of images in which the noise component between two successive images is supposed to be non-correlated. The temporal filtering techniques are essentially based either on motion detection or motion estimation. The filter structure can be either infinite impulse response (IIR) or finite impulse response (FIR) with frame delay elements. In general, the temporal techniques are more performing than the spatial ones. The system cost is essentially due to the frame memory and the motion estimation hardware. Temporal de-noising techniques can be found, for example, in U.S. Pat. No. 5,161,018 to Matsugana; U.S. Pat. No. 5,191,419 to Wicshermann; U.S. Pat. No. 5,260,775 to Farouda; U.S. Pat. No. 5,404,179 to Hamasaki and in a recent publication entitled “
The Digital Wetgate: A Third-Generation Noise Reducer
”, Wischerman; G., SMPTE Journal, February 1996, pp. 95-100.
Spatial noise reducing techniques can be applied for either still pictures or sequence of images. These techniques are described in many available textbooks such as: “
Fundamentals of Electronic Image Processing
”, Weeks; A. R. Jr., SPIE Optical Engineering Press, Bellingham, Wash., 1996; “
Two
-
Dimensional Signal and Image Processing
”, Lim; J. S., Prentice-Hall, Englewood Cliffs, N.J., 1990; and “
Nonlinear Digital Filters: Principles and Applications
”, Pitas, J. and al., Kluwer Academic Publishers, Boston, 1990. In general, spatial noise reducing techniques can be divided further into three categories.
In the first category, the spatial nonlinear filters are based on local order statistics. Utilizing a local window around a considered pixel, these filters are working on this set of pixels ordered from their minimum to their maximum values. For example, the median filter, the min/max filter, the alpha-trimmed mean filter, and their respective variants can be classified in this category. These filters work well for removing impulse like salt-and-pepper noise. However, for the small amplitude noise these filters can blur some details or small edges.
In the second category the coring techniques are applied in another domain different from the original image spatial domain. The chosen domain partly depends on noise nature. U.S. Pat. No. 4,163,258 to Ebihara teaches the use of the Walsh-Hadamard transform domain, while U.S. Pat. No. 4,523,230 to Carlson et al. discloses some sub-band decomposition. Finally, the homomorphic filter, working in the logarithmic domain, is the classical one for removing multiplicative noise and shading from an image.
In the third category, the filters are locally adaptive and the noise removing capacity is varying from homogenous regions to edge regions. These filters give good results for additive Gaussian noise. A well-known filter in this category is the minimum-mean-square-error (MMSE) filter as originally published in “
Digital image enhancement and noise filtering by use of local statistics
”, Lee; J. S., IEEE Trans. on PAMI-2, March 1980, pp. 165-168. Referring
FIG. 1
, a general block diagram of the prior art Lee's MMSE noise reducer is illustrated. Let a fixed dimension window centered on the considered or current pixel. The filtered pixel output f*(x,y) is additively composed of the local mean value obtained at output
12
of a mean estimator
10
and a weighted difference of the noisy pixel g(x,y) and the local mean intensity values. The optimum weight K determined by MMSE at an output
14
, which corresponds to a kind of coring technique, is equal to the local variance ratio of the true image and the noisy one. The Lee's MMSE filter efficiently removes noise in homogenous image regions while reserving the image edges. However, the noise essentially remains in edge or near-edge regions. Moreover, the required variance calculation is expensive for hardware implementation.
In “
One-dimensional processing for adaptive image restoration
”, Chan; P. et al., IEEE Trans. on ASSP-33, February 1985, pp. 117-126, there is presented a method for noise reducing in edge regions. The authors propose the use, in series, of four (4) one-dimensional MMSE filters respectively along 0°, 45°, 90° and 135° directions. The obtained results are impressive for large variance noise. However, for small noise, the filter can blur some image edges. Moreover, the noise variance output estimation at each filter stage require costly hardware.
For a same purpose, in “
Digital image smoothing and the Sigma filter
”, Lee; J. S., Computer Vision, Graphics, and Image Processing-24, 1983, pp. 255-269, there is proposed a said Sigma filter as illustrated in FIG.
2
. For noise removing, this filter calculates with a segmentation processor 16 using a local window of 5×5 dimensions combined to a mean estimator 18, the mean value of similar pixel intensities to that of a central considered pixel g(x,y), to obtain f*(x,y). A pixel in the window is said similar to the considered pixel if the intensity difference between these two pixels is smaller than a given threshold value. Usually, the threshold value is set equal to twice the noise standard deviation. For small noise, the Sigma filter works well, except for some pixels with sharp spot noise. For the latter case, J. S. Lee has suggested also, in a heuristic manner, the use of immediate neighbor average at the expense of some eventually blurred picture edges. Generally, the MMSE filters yield better objective results in term of peak signal to noise ratio (PSNR), than the Sigma filter.
Independently to the Lee's contribution, U.S. Pat. No. 4,573,070 to Cooper essentially discloses a Sigma filter for a 3×3 window. Moreover, in the same Patent, Cooper finally combines in a single configuration the said Sigma filter, an order statistic filter and a strong impulse noise reduction filter.
In the above-cited publication of A. R. Weeks Jr., there is described the adaptive double-window-modified-trimmed mean (DW-MTM) filter. This filter yields as output the trimmed mean value of similar pixel intensities to the median value in a local window. The adaptive DW-MTM filter is able to eliminate both salt-and-pepper noise and Gaussian noise but at the expense of filtered image blurring.
Finally, for correlated noise such as ringing/quantified noise in Discrete-Cosine-Transform (DCT) based decompressed image or cross-luminance noise in NTSC/PAL decoded image, there is still a need for a generic configuration or technique for implementing a real time noise reducer.
SUMMARY OF THE INVENTION
It is therefore an object of the present invention to provide an apparatus and a method for adaptively reducing noise in a noisy input image signal which provides spatial noise reduction in both homogenous and edge regions using a robust adaptive local segmented window, while preserving picture edges.
Another object of an aspect of the present invention is to provide, via a local segmented window, a technique that can adaptively estimate local mean and local standard deviation or variance in a non-stationary environment of a picture.
Yet another object of an aspect of the present invention is to provide an economic MMSE-based spatial noise reducer.
Yet another object of an aspect of the present invention is to provide a combined spatial noise reducer in which multiple local segmented windows are considered.
Yet another object of an aspect of the present invention is to pro
Dinh Chon Tam Le
Kieu Cong Toai
Viet Ha Do
(Ogilvy Renault)
Kassa Yosef
Mehta Bhavesh M.
Miranda Technologies Inc.
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