Apparatus and method for filtering digital image signal

An apparatus and method of filtering a digital image signal. The apparatus includes: a noise reduction filter which selectively outputs one of results obtained by temporally and spatially filtering pixel values of pixels of each of frames of an image as a temporal or spatial filtering value in response to magnitudes of the results of temporal and spatial filtering; and a sharpness enhancement filter which highlights and outputs a high pass component of the temporal or spatial filtering value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of Korean Patent Application No. 2004-31319, filed on May 4, 2004 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing device such as a digital television (DTV), a multi-function monitor, or a display system on chip (SoC), and more particularly, to an apparatus and method of filtering a digital image signal.

2. Description of the Related Art

In general, a transmitter transmits a digital image signal which necessarily includes a noise component due to the characteristics of a transmission channel or a display device. A receiver may improve such a noise component using a signal processing technique.

Most of the conventional methods of filtering a digital image signal to reduce noise using a signal processing technique adopt 2-dimensional spatial filtering or 1-dimensional temporal filtering. In addition, there have recently been suggested techniques for using motion compensation to exactly extract inter-field correlation information from a moving image sequence. However, in such a conventional digital image signal filtering method, a noise component deteriorates the accuracy of motion estimation. Also, such motion estimation includes complicated operations and increases the complexity of hardware.

Moreover, an image, which has passed through a noise reduction filter, is generally less sharp than the input image. This results from the characteristics of a low-pass filter (LPF) of the general noise reduction filter. Thus, most conventional digital image signal filtering methods perform noise reduction filtering and then adopt a sharpness enhancement filter, which enhances a high pass component, so as to prevent the deterioration of the sharpness of an image.

Conventional methods using spatial filtering are disclosed in a paper entitled “Noise Reduction for MPEG type of Code” by L. Yan, 1994 and published in IEEE International Conference Acoustic, Speech and Signal Processing, a paper entitled “I.McIC:a Single-chip MPEG-2 Video Encoder for Storage” by A. van der Werf, November, 1997 and published in IEEE Journal of Solid-State Circuits, vol. 32, No. 11, and European Patent No. EP0878776 by Mancuso et al. published on Aug. 27, 2003. The conventional spatial filtering method disclosed in European Patent No. EP0878776 determines whether a pixel to be filtered is a smooth pixel using a fuzzy logic process so as to adaptively employ low-pass filtering. However, such a conventional spatial filtering method deteriorates filtering performance when a noise level is high.

Also, a conventional method using motion compensation is disclosed in a paper entitled “Noise Reduction Filters for Dynamic Image Sequences: a Review” by Katsaggelos, September 1996 and published in Processings of IEEE, vol. 83. The disclosed method requires a large amount of operations to be executed due to excessively repeated operations and thus is costly and has difficulty in real-time realization.

A conventional method using temporal filtering is disclosed in a paper entitled “Noise Reduction in Image Sequences Using Motion Compensated Temporal Filtering” by E. Dubois et al., July 1984 and published in IEEE Trans. On Communications, vol. COM-32, p 826-831. In the disclosed method, an image is roughly divided based on a distance between a central pixel and a neighboring pixel in order to prevent pixels included in different regions in a window from being filtered. Only a minimal amount of blurring occurs at the edge of an image, in a method using temporal filtering, compared to a spatial filtering method. However, the deterioration of image quality by artifacts such as ghost tail occurs with an increase in the number of frames used for filtering.

A conventional temporal filtering method, which is an improvement of Dubois technique, is disclosed in a paper entitled “A Method of Noise Reduction on Image Processing” by S. Inamori et al., November, 1993 and published in IEEE Trans. on Consumer Electorinics, vol. 39, No. 4. In the disclosed method, a filtering function is turned on or off through motion detection and edge detection to prevent a ghost tail from occurring. However, the ghost tail still occurs and particularly, the filtering function is turned off even when a noise peak appears.

Furthermore, the previously-described conventional methods mostly reduce noise using a low-pass filtering technique. As a result, the sharpness of a filtered result deteriorates.

SUMMARY OF THE INVENTION

In accordance with an aspect of the present invention an apparatus to filter a digital image signal to efficiently and economically reduce noise and to improve the sharpness of an image is provided.

In accordance with an aspect of the present invention, a method of filtering a digital image signal to efficiently and economically reduce noise and to improve the sharpness of an image is provided.

According to an aspect of the present invention, there is provided an apparatus to filter a digital image signal, including: a noise reduction filter which selectively outputs one of results obtained by temporally or spatially filtering pixel values of pixels of each frame of an image as a temporal or spatial filtering value in response to magnitudes of the results of the temporal or spatial filtering; and a sharpness enhancement filter which highlights and outputs a high pass component of the temporal or spatial filtering value.

According to another aspect of the present invention, there is provided a method of filtering a digital image signal, including: selectively determining one of results obtained by temporally and spatially filtering pixel values of pixels of each of frames of an image as a temporal or spatial filtering value in response to magnitudes of the results of temporal and spatial filtering; and highlighting a high pass component of the temporal or spatial filtering value.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1is a block diagram of an apparatus filtering a digital image signal, according to an embodiment of the present invention. Referring toFIG. 1, the apparatus includes a noise reduction filter10and a sharpness enhancement filter12.

FIG. 2is a flowchart explaining a method of filtering a digital image signal, according to an embodiment of the present invention. Referring toFIG. 2, the method includes in operation14selecting one of the results, which are obtained by temporally and spatially filtering pixel values, and operation16highlighting a high pass component of the selection result.

In operation14, the noise reduction filter10ofFIG. 1selects one of the results of temporal and spatial filtering for pixel values of each frame of an image received via an input node IN1according to magnitudes of the results, and then the selected result is transferred to the sharpness enhancement filter12which highlights the high pass component in operation16.

FIG. 3is a block diagram of an embodiment10A of the present invention of the noise reduction filter10ofFIG. 1, including a temporal filter20, a spatial filter22, and a filter selector24.

The temporal filter20ofFIG. 3temporally filters pixel values input via an input node IN2and outputs the results of the temporal filtering to the filter selector24.

The spatial filter22spatially filters the pixel values input via the input node IN2and outputs the results of the spatial filtering to the filter selector24.

The filter selector24compares each magnitude of the results of the temporal and spatial filtering input, respectively, from the temporal and spatial filters20and22with a local spatial mean value. The filter selector24selects one of the results of the temporal and spatial filtering as a temporal or spatial filtering value, which is as a final result of noise filtering, in response to the comparison result, and outputs the selection result via an output node OUT2. For example, the filter selector24can select the temporal or spatial filtering value using Equation 1:

FIG. 4is a view explaining an example of a ghost tail. Referring toFIG. 4, in a case of a conventional method only using temporal filtering, the quality of an image may deteriorate. The ghost tail34occurs when the edge of a slow varying slope36of a luminance value of a t−1thframe30overlaps with a tthframe32. Since the fixed motion detection threshold is generally used, in a case where the variation of the luminance value is not abrupt but slowly varying, the ghost tail occurs and deteriorates the quality of image. However, in the apparatus and method of filtering the digital image signal, according to the embodiments of the present invention, when the results of the temporal filtering of the pixel values are larger than the local spatial mean value, a ghost tail artifact is highly likely to occur. Thus, the results of the spatial filtering of the pixel values are selected instead of the results of the temporal filtering of the pixel values as shown in Equation 1 above.

The structure and operation of an embodiment of the present invention of the temporal filter20ofFIG. 3will now be explained with reference to the attached drawings.

FIG. 5is a block diagram of an embodiment20A of the temporal filter20shown inFIG. 3, including a pixel value controller40, a pixel value operator42, and a pixel value selector44.

According to an aspect of the present invention, the pixel value controller40subtracts a pixel value of a previous frame input via an input node IN4from a pixel value of a current frame input via an input node IN3, compares the subtraction result with a motion detection threshold value, and outputs the comparison result as a pixel value control signal to the pixel value selector44. Here, the pixel value operator42generates a new pixel value using the pixel values of the current and previous frames input via the input nodes IN3and IN4and outputs the new pixel value to the pixel value selector44. The pixel value operator42may determine a mean of the pixel values of the current and previous frames input via the input nodes IN3and IN4and use the mean as the new pixel value. The pixel value selector44selects the pixel value of the current frame input via the input node IN3or the new pixel value input from the pixel value operator42in response to the pixel value control signal received from the pixel value controller40and outputs the selection result via an output node OUT3. For example, if it is perceived through the pixel value control signal that the subtraction result is smaller than or equal to the motion detection threshold value, the pixel value selector44selects the new pixel value. If it is perceived through the pixel value control signal that the subtraction result is larger than the motion detection threshold value, the pixel value selector44selects the pixel value of the current frame. When the subtraction result is smaller than or equal to the motion detection threshold value motion of an object in the image occurs infrequently, while when the subtraction result is larger than the motion detection threshold value the motion of an object in the image occurs frequently. Thus, when the occurrence of the motion of the object is rare, a noise recognition degree of a human's vision is high. In this case, the temporal filter20A selects and outputs the new pixel value from which noise has been reduced. When the occurrence of the motion of the object is frequent, the noise recognition degree of the human's vision is low. In this case, the temporal filter20A selects and outputs the pixel value of the current frame. As a result, the occurrence of blurring is reduced.

According to another aspect of the present invention, the pixel value controller40subtracts the temporal or spatial filtering value of the previous frame, which is input from the filter selector24via the input node IN4, from the pixel value of the current frame input via the input node IN3, compares the subtraction result with the motion detection threshold value, and outputs the comparison result as the pixel value control signal to the pixel value selector44. Here, the pixel value operator42adds a weight on the pixel value of the current frame input via the input node IN3and the temporal or spatial filtering value of the previous frame input via the input node IN4to generate at least one new pixel value and outputs the at least one new pixel value to the pixel value selector44. The pixel value selector44selects the pixel value of the current frame input via the input node IN3or the new pixel value input from the pixel value operator42in response to the pixel value control signal input from the pixel value controller40and outputs the selection result via the output node OUT3. For example, the temporal filter20A can temporally filter pixel values using Equation 2:

As can be seen in Equation 2, when a difference between the pixel value of the current frame and the temporal or spatial filtering value of the previous frame is low, more weight is added on the temporal or spatial filtering value of the previous frame to realize continuous image quality. When the difference between the pixel value of the current frame and the temporal or spatial filtering value of the previous frame is not low, more weight is added on the pixel value of the current frame.

The structure and operation of an embodiment of the present invention of the spatial filter22ofFIG. 3will now be described with reference to the attached drawings.

FIG. 6is a block diagram of an embodiment22A of the spatial filter22ofFIG. 3, including a line delayer60, a first multiplier62, a subtracter64, a reference value generator66, a comparator68, and a lookup table (LUT)70.

According to an aspect of the present invention, the spatial filter22A may include only the line delayer60and the first multiplier62.

The line delayer60receives a temporal or spatial filtering value, which is generated for a neighboring pixel belonging to a previous line inside a window mask having a predetermined size, from the filter selector24via an input node IN5to delay the received temporal or spatial filtering value by a unit line and outputs the delay result to the first multiplier62. Here, the previous line refers to a line which is located prior to a current line, the current line refers to a line in which a central pixel is located, the central pixel refers to a pixel which is located in the center of the window mask, and the neighboring pixel refers to a pixel which neighbors the central pixel.

The first multiplier62multiplies pixel values of neighboring pixels belonging to a previous line input from the line delayer60among pixels inside the widow mask or pixel values of neighboring pixels belonging to a subsequent line input from an external source via an input node IN6among pixels inside the window mask by corresponding weights, accumulates the multiplication results, and outputs the accumulation result as a result of spatial filtering of a pixel value of the central pixel via an output node OUT4. Here, the subsequent line refers to a line which is located after the current line. For example, the first multiplier62can obtain the result of spatial filtering of the pixel value of the central pixel using Equation 3:

fσ⁡(x→,t)=k⁢⁢∑n→∈W⁢v⁡(x→,n→)×f⁡(x→+n→,t)(3)
wherein “fσ({right arrow over (x)},t)” denotes the result of spatial filtering of the pixel value of the central pixel, k denotes a constant, “v({right arrow over (x)},{right arrow over (n)})” denotes a weight, “f({right arrow over (x)}+{right arrow over (n)},t)” denotes a pixel value of a neighboring pixel belonging to the previous or subsequent line, W denotes the window mask, and “{right arrow over (n)}” denotes a spatial position of a neighboring pixel inside the window mask.

FIG. 7is an exemplary view of a 3×5 window mask to aid in the comprehension of the spatial filter22A ofFIG. 6, including a previous line90, a current line92, and a subsequent line94.

The line delayer60ofFIG. 6receives temporal or spatial filtering values, which are generated for neighboring pixels80and82belonging to the previous line90inside the 3×5 window mask ofFIG. 7, from the filter selector24via the input node IN5to delay the received temporal or spatial filtering value by a unit line and outputs the delay result to the first multiplier62.

The first multiplier62multiplies pixel values f({right arrow over (x)}+{right arrow over (n)},t) of the neighboring pixels80and82belonging to the previous line90input from the line delayer60among pixels inside the 3×5 window mask ofFIG. 7or pixel values f({right arrow over (x)}+{right arrow over (n)},t) of neighboring pixels86and88belonging to the subsequent line94input from the external source via the input node IN6among pixels inside the 3×5 window mask by corresponding weights, accumulates the multiplication results, and outputs the accumulation result as a result of spatial filtering of a pixel value of a central pixel84via the output node OUT4.

As previously described, spatial filtering is performed using only five pixels80,82,84,86, and88of pixels inside the 3×5 window mask. The reason why all of the pixels inside the 3×5 window mask are not used is that the spatial filter22A according to aspects of the present invention has a recursive structure in which temporal or spatial filtering values for the neighboring pixels80and82belonging to the previous line90are used to enhance noise reduction efficiency. As a result, since the pixel value of the central pixel84is spatially filtered using the recursive structure, oversmoothing can be minimized.

According to another aspect of the present invention, as shown inFIG. 6, in order to generate the weight, the spatial filter22A may further include the subtracter64, the reference value generator66, the comparator68, and the LUT70.

Here, the subtracter64subtracts a pixel value of a neighboring pixel input via the input node IN5or IN6from a pixel value of a central pixel input via an input node IN7and outputs the subtraction result to the comparator68.

The reference value generator66predicts a noise variance value from the pixel value of the central pixel input via the input node IN7, generates a reference weight value using the predicted noise variance value, and outputs the reference weight value to the comparator68.

FIG. 8is a block diagram of an embodiment66A of the reference value generator66ofFIG. 6, including a variance value predictor100, a frame delayer102, and an operator104.

The variance value predictor100predicts a noise variance value from a pixel value of a central pixel input via an input node IN8and outputs the predicted noise variance value to the frame delayer102.

FIG. 9is a block diagram of an embodiment100A of the variance value predictor100ofFIG. 8, including a first pixel value difference calculator120, a mean value calculator122, and a maximum histogram detector124.

The first pixel value difference calculator120calculates a difference between a pixel value of each of the pixels inside a window mask and a median pixel value and outputs the difference to the mean value calculator122. For this purpose, the first pixel value difference calculator120may receive all the pixels inside the window mask via an input node IN9and calculate the median pixel value from all the pixels. Here, the mean value calculator122calculates a mean value of the differences calculated by the first pixel value difference calculator120and outputs the mean value to the maximum histogram detector124. For example, the mean value calculator122calculates the mean value using Equation 4:

Here, the maximum histogram detector124receives the mean value from the mean value calculator122, predicts a mean value having a maximum histogram from a noise variance value, and outputs the predicted noise variance value via an output node OUT6. For example, the maximum histogram detector124obtains the noise variance value using Equation 5:
Nvar=MAX{Histogram[S({right arrow over (x)})]}  (5)
wherein Nvardenotes the noise variance value, and MAX denotes a maximum value.

The frame delayer102ofFIG. 8delays the noise variance value predicted by the variance value predictor100by a unit frame and outputs the delayed noise variance value to the operator104. The operator104generates a reference weight value using the delay result of the frame delayer102and outputs the reference weight value to the comparator68via an output node OUT5. The operator104generates the reference weight value using Equation 6:

σ=σin10×Nvar+0.5(6)
wherein σ denotes the reference weight value, and σindenotes a predetermined initial value.

The comparator68compares the subtraction result of the subtracter64with the reference weight value generated by the reference value generator66and outputs the comparison result to the LUT70. The LUT70receives as an address the comparison result from the comparator68, stores the weight as data, and outputs data corresponding to the address input from the comparator68to the first multiplier62.

For example, the subtracter64, the reference value generator66, the comparator68, and the LUT70ofFIG. 6generate the weight using Equation 7:

As shown in Equation 7, as the result a of the subtraction of a pixel value of a neighboring pixel from a pixel value of a central pixel is low, the correlation between the central pixel and the neighboring pixel is highly likely to be high. Thus, the weight [v(a)] is generated to be high.

Accordingly, the reference weight value a most greatly affects the filtering results of the spatial filter22A. In other words, when the noise variance value is large, the reference weight value is set to be large so as to enhance noise removal efficiency. When the noise variance value is not large, the reference weight value is lowered to prevent blurring. For this purpose, a noise variance value is predicted for data in a previous frame to adaptively vary a predetermined initial value σinaccording to the noise degree of the previous frame as in Equation 6. Here, a noise variance value of a current frame must be predicted to maximize noise reduction efficiency. However, when the noise variance value of the current frame is predicted, one frame delay necessarily occurs at an output node and one additional frame memory is required to store data in the current frame. Thus, on the assumption that a noise component is stationary, the noise variance value predicted for data in the previous frame may be used for reducing noise of the current frame.

InFIG. 2, after operation14, in operation16, the sharpness enhancement filter12ofFIG. 1highlights a high pass component of the temporal or spatial filtering value input from the noise reduction filter10and outputs the highlighted result via an output node OUT1.

Here, the temporal or spatial filtering value output from the noise reduction filter10may be in the form of red (R), green (G), and blue (B). In this case, for color balancing at a gray edge, the sharpness enhancement filter12may transform the temporal or spatial filtering value in the form of RGB into a color space of YCbCr (where Y denotes a luminance component, and CbCr denote chrominance components, respectively) and highlight the high pass component using only the transformed luminance component Y. Here, while the high pass component is highlighted for the luminance component Y, the chrominance components CbCr are delayed. Thereafter, the highlighted result of the high pass component for the luminance component Y and the delayed chrominance components CbCr are recovered to the RGB form. For example, the sharpness enhancement filter12may include a transformer (not shown) which transforms the RGB form into the color space of YCbCr, a delayer (not shown) which delays the chrominance components CbCr, and a recovery unit (not shown) which recovers the color space of YCbCr to the RGB form. However, the transformer, the delayer, and the recovery unit may be separately installed outside the sharpness enhancement filter12.

FIG. 10is a block diagram of an embodiment12A of the sharpness enhancement filter12ofFIG. 1, including a high pass component extractor140, a gain determiner142, a second multiplier144, and a synthesizer146.

The high pass component extractor140ofFIG. 10receives a temporal or spatial filtering value for a central pixel via an input node IN10, extracts a high pass component of the temporal or spatial filtering value, and outputs the high pass component to the second multiplier144.

FIG. 11is a block diagram of an embodiment140A of the high pass component extractor140ofFIG. 10, including a second pixel value difference calculator160, a parameter determiner162, a correlation calculator164, and a high pass component calculator166.

The second pixel value difference calculator160ofFIG. 11receives a pixel value of a central pixel and a pixel value of a neighboring pixel via an input node IN12, calculates a difference between the pixel values of the central and neighboring pixels, and outputs the difference to the parameter determiner162and the correlation calculator164. Here, the neighboring pixel refers to a pixel which neighbors the central pixel.

Here, the parameter determiner162receives the difference from the second pixel value difference calculator160, calculates a variation range of the difference, determines a parameter corresponding to the variation range of the difference, and outputs the parameter to the correlation calculator164. Here, the parameter determiner162outputs the variation range of the difference via an output node OUT8. For example, the parameter determiner162determines the parameter using Equation 8:

The correlation calculator164calculates a correlation between the central and neighboring pixels using the parameter input from the parameter determiner162and the difference input from the second pixel value difference calculator160and outputs the correlation to the high pass component calculator166. For example, the correlation calculator14calculates the correlation using Equation 11:

FIG. 12is a graph showing the relationship between the parameter and the correlation. Here, the vertical axis denotes the correlation Wcorr(m,n) and the horizontal axis denotes the difference D(m,n) calculated by the second pixel value difference calculator160.

Referring to Equations 11 and 12, when the difference D(m,n) input from the second pixel value difference calculator160is large, the correlation Wcorr(m,n) becomes low. When the difference D(m,n) is small, the correlation Wcorr(m,n) becomes high. In other words, the difference D(m,n) and the correlation Wcorr(m,n) have a Gaussian function relation. Here, the parameter Δ serves to determine a Gaussian function range. For example, as the parameter Δ is large, the Gaussian function range increases in the order of e1, e2, e3, and e4. For example, the Gaussian function range e1, e2, e3, or e4 corresponds to the parameter Δ of 5, 10, 15, or 20, respectively. In other words, when the difference D(m,n) is fixed, the correlation Wcorr(m,n) becomes high with an increase in the parameter Δ. As a result, the amplification of a noise component can be reduced by increasing the correlation Wcorr(m,n).

The high pass component calculator166calculates a high pass component using the correlation input from the correlation calculator164and the temporal or spatial filtering value input via the input node IN12and outputs the high pass component via an output node OUT9. The high pass component calculator166is a kind of high-pass filter (HPF). Here, since the sum of all coefficients in a filter mask must be zero, a final coefficient of the HPF is calculated as in Equation 12:

In other words, for an M′×N′ filter mask, a difference between a mean value of all correlation values and each of the correlation values is calculated so as to have the characteristics of the HPF. Here, the high pass component calculator166can calculate the high pass component using Equation 13:

yHPF⁡(i,j)=∑(m,n)∈M′×N′⁢{[Wcorr⁡(m,n)-1M′×N′⁢⁢∑(m,n)∈M′×N′⁢Wcorr⁡(m,n)]×z⁡(m,n)}(13)
wherein “yHPF(i,j)” denotes the high pass component.

As can be seen in Equation 13, when the difference D(m,n) between the pixel values of the central and neighboring pixels is large, the correlation Wcorr(m,n) is low. Thus, a result of the subtraction of a mean correlation from the correlation Wcorr(m,n) has a negative value. In the opposite case, the result of the subtraction of the mean correlation from the correlation Wcorr(m,n) has a positive value. As a result, the high pass component can be efficiently extracted. When the high pass component is extracted, an increase in the parameter Δ can contribute to enhancing an efficiency of preventing a fine noise component from being amplified in a background region of an image. However, in a case of an edge region having a large difference between pixel values of a central pixel and a neighboring pixel, it is quite probable that the deterioration of image quality, such as overshooting or undershooting, will occur and a fine difference between grayness values of an image becomes smooth due to a large parameter Δ. As a result, an output image is unnaturally generated. Therefore, the parameter determiner162of the high pass component extractor140A determines the parameter Δ depending on the variation range Drangeof the difference as in Equation 8 above. In other words, the parameter Δ is set to be high in a smooth region in which the variation range Drangeof the difference is smaller than or equal to the first predetermined threshold value th1, while the parameter Δ is set to be low in an edge region in which the variation range Drangeof the difference is larger than the first predetermined threshold value th1.

A conventional method of extracting a high pass component using Laplacian- and Gradient-based high frequency component extracting techniques is disclosed in a book entitled “Fundamentals of Digital Image Processing” by Anil K. Jain and published by Prentice-Hall International Edition, 1989, p. 347-357. When the disclosed conventional method is adopted, noise sensitivity is high. Thus, a noise component of an image passing through the noise reduction filter10may be highlighted causing deterioration in the quality of the image. To solve such a problem, the sharpness enhancement filter12, according to aspects of the present invention, determines similarity between pixels using a difference D(m,n) between pixel values of a central pixel and a neighboring pixel as previously described and non-linearly determines a coefficient of a HPF using the similarity.

Meanwhile, the gain determiner142ofFIG. 10determines a gain using a variation range of a difference between a pixel value of a central pixel and pixel values of neighboring pixels inside a filter mask and outputs the gain to the second multiplier144, which multiplies the gain with the extracted high pass component. For the purpose of determining the gain, the gain determiner142may receive the variation range of the difference from the high pass component extractor140, (i.e., from the parameter determiner162of the high pass component extractor140A), or may obtain the variation range of the difference using the temporal or spatial filtering value for the central pixel input via the input node IN10.

FIG. 13is a block diagram of an embodiment142A of the gain determiner142ofFIG. 10, including a gain calculator200and a gain adjuster208.

FIG. 14is an exemplary graph of the present invention for showing the relationship of a gain to the variation range Drangeof the difference. Here, the horizontal axis denotes the variation range Drangeof the difference, and the vertical axis denotes the gain.

For example, as shown inFIG. 14, the gain calculator200linearly determines a gain. For example, the gain calculator200determines the gain to be low so as to prevent a noise component from being amplified in a region in which the variation range Drangeof the difference is small, determines the gain to be inversely proportional to the variation range Drangeof the difference so as to prevent overshooting or undershooting from occurring in a region in which the variation range Drangeof the difference is large, and determines the gain to be high so as to enhance sharpness of an image in a region in which the variation range Drangeof the difference is median.

For example, the gain calculator200can determine the gain using Equation 14:

FIG. 15is an exemplary view explaining the generation of overshooting and undershooting, including blocks210and212.

When the gain determiner142A ofFIG. 13includes only the gain calculator200, overshooting or undershooting cannot be completely removed. For example, the block210ofFIG. 15is classified as a smooth region, and thus a high frequency component is almost close to zero therein. However, a considerably large high frequency component is extracted from the block212. A human's vision is sensitive to overshooting of the block212. The gain calculator200substantially reduces a high frequency amplification gain for the block212. However, a significant amount of high frequency component is added to a source image on account of a strong edge component of the block212, thereby overshooting may occur. Since a gain of a high frequency domain cannot be sharply reduced to obtain the continuity of image quality, the block212must be additionally processed. For this purpose, the gain determiner142A ofFIG. 13may further include the gain adjuster208besides the gain calculator200. Here, the gain adjuster208detects variation ranges of differences for all pixels of a block input via an input node IN13, adjusts a gain using the variation ranges of the differences, and outputs the adjusted gain via an output node OUT10. For this purpose, the gain adjuster208may include a variation range detector202, a variation degree calculator204, and a gain attenuator206.

Here, the variation range detector202detects a variation range of a difference between a pixel value of each of all of the pixels belonging to a block having a neighboring pixel as a central pixel and a pixel value of each of the neighboring pixels.

The variation degree calculator204calculates a difference between maximum and minimum values of the variation range of the difference detected by the variation range detector202and outputs the difference to the gain attenuator206.

FIG. 16is an exemplary view to aid in the comprehension of the gain adjuster208ofFIG. 13, including a central pixel250and neighboring pixels252,254,256,258,260,262,264, and266which are marked with striped circles and pixels which are marked with empty circles.

The variation range detector202obtains a variation range Dranges of a difference between a pixel value of each of the pixels belonging to a block300,302,304,306,310,312,314, or316, which has a neighboring pixel252,254,256,258,260,262,264, or266as a central pixel, respectively, and a pixel value of the neighboring pixel252,254,256,258,260,262,264, or266using Equation 15. Here, in a case ofFIG. 16, 1≦s≦8.
Dranges=MAX{Ds(p,q)}−MIN{Ds(p,q)}  (15)
wherein, when a block has the size of P×Q, 1≦p≦P, 1≦q≦Q, and in the case ofFIG. 16, P=Q=3, MIN denotes a minimum value, and Ds(p,q) denotes the difference between the pixel value of each of the pixels belonging to the block300,302,304,306,310,312,314, or316, which has the neighboring pixel252,254,256,258,260,262,264, or266as the central pixel, respectively, and the pixel value of the neighboring pixel252,254,256,258,260,262,264, or266and can be represented as in Equation 16:
Ds(p,q)=|z(p,q)−z(i,j)  (16)
wherein z(p,q) denotes a pixel value of a central pixel, and z(i,j) denotes a pixel value of a neighboring pixel.

Thus, the variation degree calculator204calculates a difference Diff_Drangebetween maximum and minimum values MAX and MIN of the variation range of the difference and can be represented as in Equation 17:
Diff—Drange=MAX(Drange1,Drange2, . . . ,Drange8)−MIN(Drange1,Drange2, . . . ,Drange8)  (17)

The gain attenuator206adjusts the determined gain using the variation range Drangeof the difference in response to the magnitude of the difference Diff_Drangecalculated by the variation degree calculator204and outputs the adjusted gain via the output node OUT10. For example, when the difference Diff_Drangeis equal to or larger than a fifth predetermined threshold value th5, the gain attenuator206can adjust the determined gain as in Equation 18:

Gfinal=Gain×Drange2c(18)
wherein Gfinal, denotes the adjusted gain output from the gain attenuator206, Gain denotes the gain determined by the gain calculator200, Drangedenotes the variation range of the difference between the pixel value of each of the pixels252,254,256,258,260,262,264, and266included in the block308having the central pixel250and the pixel value of the central pixel250, and c denotes the number of pixels neighboring the central pixel250. Here, reference numerals ofFIG. 16are used to aid in the comprehension of Equation 18, but Equation 18 is not limited toFIG. 16.

The second multiplier144multiplies the gain determined by the gain determiner142by the high pass component extracted by the high pass component extractor140and outputs the multiplication result to the synthesizer146. The synthesizer146synthesizes the multiplication result of the second multiplier144and an image input via an input node IN11, i.e., the temporal or spatial filtering value, and outputs the synthesis result as a sharpened result via an output node OUT7.

FIGS. 17A and 17Bare views illustrating exemplary outputs of the present invention and the conventional method.FIG. 17Aexemplarily shows an image filtered using a conventional digital image signal filtering apparatus and method, andFIG. 17Bexemplarily shows an image filtered using a digital image signal filtering apparatus and method according to an embodiment of the present invention.

As shown inFIG. 17A, when a pixel value is only temporally filtered, a ghost tail400occurs, which deteriorates the quality of the image. However, as shown inFIG. 17B, when one of the results, which are obtained by temporally and spatially filtering the pixel value, is selected and enhanced, a ghost tail disappears in a region402in which the ghost tail400has occurred.

As described above, in an apparatus and method of filtering a digital image signal, according to embodiments of the present invention, the noise reduction filter10can be used to adaptively adopt 2-dimensional spatial filtering and 1-dimensional temporal filtering according to the characteristics of an image signal. Thus, oversmoothing and motion blurring can be prevented from occurring at the edge of an image so as to solve a ghost tail problem. Also, noise level estimation can be performed to efficiently reduce noise without deteriorating the quality of the image even when a noise level is high. Moreover, a high pass component can be extracted adaptively to the characteristics of a temporal or spatial filtering value so as not to excessively amplify a remaining noise component and so as to enhance sharpness of the image. As a result, the digital image signal can be filtered at a low cost and at a low operation complexity.