Image enhancement device and method thereof

Abstract

The invention provides an image enhancement device and a method thereof. The device comprises N line buffers, a local pane statistics circuit, a tone mapping circuit, an edge enhancement circuit and an adaptive processing circuit. The N line buffers are used for receiving N rows of pixels of a current image and outputting N2 pixels according to an N * N local pane. The local pane statistics circuit performs low-pass filtering operation on the values of the N2 pixels to generate a filtering value mu, and calculates a difference value delta between the filtering value mu and a value of a central pixel of the N2 pixels. The tone mapping circuit generates a mapping value Ymap according to the brightness component [mu] Y of the filtering value [mu] and the coordinate of the central pixel; the edge strengthening circuit is used for carrying out edge strengthening filtering operation on the brightness components of the N2 pixels so as to generate a brightness difference value delta EE; the adaptive processing circuit updates the brightness component of the center pixel according to the four outputs [mu] Y, [delta] Y, Ymap and [delta] EE.

Description

Technical Field

[0001] This invention relates to image processing, and more particularly, to an image enhancement apparatus and method capable of simultaneously performing noise reduction, edge enhancement, and local contrast enhancement. Background Technology

[0002] Image enhancement is the process of improving the visual quality, sharpness, and contrast of an image. It typically involves smoothing the image in uniform areas while preserving edges, and sharpening features to simplify image interpretation for both human observers and machines. A typical image enhancement pipeline consists of three consecutive stages: noise reduction, edge enhancement, and local contrast enhancement, and these three stages can be executed in different orders. Depending on the size (N×N) of the expected window or filter used in each stage, N line buffers are used in a corresponding stage to temporarily store N pixels in an image, keeping data in the periphery to reduce the overall memory bandwidth required. These line buffers are typically implemented using static random access memory (SRAM). For example, if N=3, Figure 1 An example is shown where a 3×3 Gaussian filter 11 slides across a current image 10 during the noise reduction stage. To use the 3×3 Gaussian filter 11, it is not necessary to store the entire current image 10; instead, only the current row of pixels and the two rows preceding it are stored in three line buffers 111–113 (not shown). After the 3×3 Gaussian filter 11 slides across three rows of pixels in line buffers 111–113 with a stride equal to one pixel, the output pixel is sent to three more line buffers 114–116 (not shown) in the next stage (e.g., the edge enhancement stage). After a 3×3 Sobel filter (not shown) slides across three rows of pixels in line buffers 114–116 with a stride equal to one pixel, the output pixel is sent to three more line buffers 117–119 (not shown) in the next stage (e.g., the local contrast enhancement stage). In this way, at least 3×N line buffers are needed to complete the entire image enhancement pipeline.

[0003] Therefore, the industry urgently needs an image enhancement architecture that can reduce hardware costs and improve computational efficiency without compromising image quality. This invention meets the above requirements. Summary of the Invention

[0004] In view of the above problems, one of the objectives of the present invention is to provide an image enhancement device that can reduce hardware costs and improve computational efficiency.

[0005] An embodiment of the present invention provides an image enhancement apparatus, comprising: N line buffers, a local pane statistics circuit, a tone mapping circuit, an edge enhancement circuit, and an adaptive processing circuit. The N line buffers are used to receive N rows of pixels from an image and output N values ​​according to an N×N local pane. 2 N×N local panes are pixels, wherein the N×N local panes are swiped across the image. The local pane statistics circuit performs calculations on the N... 2 The values ​​of each pixel are subjected to a low-pass filter to generate a filtered value μ, and a difference Δ is calculated between the filtered value μ and the value of a center pixel, wherein the center pixel is located at the center point of the N×N local pane. The tone mapping circuit, based on the luminance component μ of the filtered value μ... Y And the coordinates of the center pixel, to generate a mapping value Y. map The edge enhancement circuit, for the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a luminance difference δ. EE The adaptive processing circuit, based on the four outputs (μ) of the local pane statistics circuit, the tone mapping circuit, and the edge enhancement circuit... Y Δ Y Y map δ EE ), to update the luminance component of the center pixel. Wherein, Δ Y The luminance component representing the difference Δ, and the local pane statistics circuit, the tone mapping circuit, and the edge enhancement circuit operate in parallel.

[0006] Another embodiment of the present invention provides an image enhancement method, comprising: storing N rows of pixels of an image using N line buffers, and outputting N based on an N×N local pane. 2 N×N local panes are pixels, wherein the N×N local panes are swiping across the image; in a local pane statistics module: for the N 2 The values ​​of each pixel are subjected to a low-pass filter to produce a filtered value μ; and a difference Δ is calculated between the filtered value μ and the value of a center pixel, wherein the center pixel is located at the center point of the N×N local pane; in a tone mapping module: based on the luminance component μ of the filtered value μ... Y And the coordinates of the center pixel, to generate a mapping value Y. map In an edge enhancement module: for the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a luminance difference δ. EE; and, in an adaptive processing module: based on the four outputs (μ) of the local pane statistics module, the tone mapping module, and the edge enhancement module. Y Δ Y Y map δ EE ), update the luminance component of the center pixel; wherein, Δ Y The brightness component representing the difference Δ, and the local pane statistics module, the tone mapping module, and the edge enhancement module operate in parallel.

[0007] The above and other objects and advantages of the present invention will be described in detail below with reference to the following illustrations, detailed descriptions of the embodiments, and the scope of the claims. Attached Figure Description

[0008] Figure 1 An example of a 3×3 Gaussian filter 11 sliding across a current image 10 during the noise reduction stage is shown.

[0009] Figure 2 This is a block diagram showing an image enhancement device according to the present invention.

[0010] Figure 3 This is a schematic diagram showing the LWS module 210 according to an embodiment of the present invention.

[0011] Figure 4 This is a schematic diagram showing the TM module 220 according to an embodiment of the present invention.

[0012] Figure 5 This is a schematic diagram showing the EE module 230 according to an embodiment of the present invention.

[0013] [Symbol Explanation]

[0014] 10 Current Images

[0015] 11 3×3 Gaussian filter

[0016] 111-119 line buffer

[0017] 200 Image Enhancement Device

[0018] 210 Local Pane Statistics Module

[0019] 220 tone mapping module

[0020] 230 Edge Reinforcement Module

[0021] 240 Adaptive Processing Module

[0022] 250 N line buffers

[0023] 260 Output Buffer

[0024] 310 Low-pass filter

[0025] 320 Deviation Calculation Module

[0026] 330 Saturation Calculation Module

[0027] 340, 520 subtractors

[0028] 410 Limit Contrast Adaptive Histogram Equalization

[0029] 411 Block Generation Module

[0030] 412 Histogram Equalization Pipeline Module

[0031] 413 Interpolation Module

[0032] 420 Tone Adjustment Module

[0033] 510 Edge Enhancement Filter

[0034] 511N×N Laplace filter

[0035] 512N×N Sober filter

[0036] 513-514 Multipliers

[0037] 515 Adder Detailed Implementation

[0038] Throughout this specification, the relevant terms are defined as follows, unless otherwise specified: The singular forms of "a" and "the" include both singular and plural meanings; the symbol " / " includes the meanings of "or" and "and". Furthermore, the term "circuit" represents a hardware element, a part of a hardware element, or a combination of multiple hardware elements; a hardware element includes, but is not limited to, an electronic circuit, a logic circuit, a processor (shared, dedicated, or grouped) and / or memory (shared, dedicated, or grouped), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a system-on-chip (SoC), and a signal processor (DSP); the aforementioned hardware elements are configured to provide the aforementioned functions. The term "module" represents a unit configured / planned with hardware (circuit), software, firmware, or any combination of the above, capable of performing the aforementioned functions. Throughout the specification, circuit elements with the same function use the same reference symbols.

[0039] One key feature of this invention is the use of only N line buffers and an N×N single local window for single-stage processing (or single-pane processing). This avoids the lengthy processing of the three consecutive stages (noise reduction, edge enhancement, and local contrast enhancement) in the aforementioned image enhancement pipeline, thereby reducing hardware costs and improving computational efficiency. Another feature of this invention is the use of a parallel-operating local window statistics module, an edge enhancement module, and a tone mapping module to process a current image. Multiple preliminary parameters are calculated on a pixel-by-pixel basis, and then an enhanced image is generated based on these preliminary parameters.

[0040] Figure 2 This is a block diagram showing the image enhancement device 200 according to the present invention. (Reference) Figure 2The image enhancement apparatus 200 of the present invention includes N line buffers 250, a local window statistics (LWS) module 210, an edge enhancement (EE) module 230, a tone mapping (TM) module 220, an adaptive processing (AP) module 240, and an output buffer 260, wherein N>2, and each of the N line buffers 250 stores a row of pixels of a current image. The N line buffers 250 are implemented using SRAM. The image enhancement apparatus 200 receives a current image through the N line buffers 250, and then uses a single local window of size N×N to slide across the N rows of pixels within the N line buffers 250 in a "step of one pixel" manner to simultaneously perform noise reduction, edge enhancement (or sharpness) and local contrast enhancement processing on the current image, generating and storing an enhanced image in the output buffer 260 on a pixel-by-pixel basis. Where N is the size of the aforementioned single local pane (e.g., the low-pass filter 310 in LWS module 210 and the Sopper filter 512 and Laplace filter 511 in EE module 230). LWS module 210, EE module 230, and TM module 220 operate in parallel. The current image is represented by the following three components: Y (luma), U (chroma), and V (color). For clarity and ease of description, the following examples and embodiments are illustrated by assuming N = 3 and a 3×3 single local pane glides across the current image in a "left-to-right and top-to-bottom" manner.

[0041] After receiving three rows of pixels from the current image, the line buffer 250 transmits a group of nine pixels to the LWS module 210 and the EE module 230 at a time, based on a group of nine pixels within a sliding 3×3 single local pane. The coordinates of the group of nine pixels, from left to right and from top to bottom, are as follows: (x-1, y-1), (x, y-1), (x+1, y-1), (x-1, y), (x, y), (x+1, y), (x-1, y+1), (x, y+1), and (x+1, y+1), where a center pixel S(x, y) is located at the center point of the 3×3 single local pane.

[0042] Figure 3 This is a block diagram showing an LWS module 210 according to an embodiment of the present invention. (See reference) Figure 3The LWS module 210 of this invention includes a low-pass filter 310, a deviation calculation module 320, a saturation calculation module 330, and a subtractor 340. The low-pass filter 310 (corresponding to the aforementioned 3×3 single local pane) performs low-pass filtering on the YUV components of the nine pixels in the group to produce a YUV component (μ) corresponding to the center pixel S(x,y). Y ,μ U ,μ V The filtered pixel value μ is calculated by taking the average value μ of the group of nine pixels. In one embodiment, the filtered pixel value μ is obtained by calculating the mean value μ of the group of nine pixels. Where n = N 2 and Y i U i and V i These represent the Y, U, and V components of pixel i in the group of nine pixels, respectively. As is well known in the industry, standard deviation is used to measure the dispersion of multiple values ​​relative to their mean. In one embodiment of the deviation calculation module 320, the following program codes are provided to calculate the standard deviation σ of the Y component of the group of nine pixels. Y :

[0043]

[0044] / / If T<0, then σ Y =0, otherwise

[0045] The saturation calculation module 330 calculates the saturation index based on the average value μ and the following equation: Finally, subtractor 340 subtracts the YUV components (Sx, y) of the center pixel S(x, y). Y ,S U ,S V Subtract the average value μ from the YUV component (μ) respectively Y ,μ U ,μ V ), producing a YUV component (Δ Y ,Δ U ,Δ V The difference Δ, that is, Δ Y =(S Y -μ Y ), Δ U =(S U -μ U ) and Δ V =(S V -μ V).

[0046] Figure 4 This is a block diagram showing a TM module 220 according to an embodiment of the present invention. (See reference) Figure 4 The present invention's TM module 220 includes a histogram equalization module 410 and a tone adjustment module 420. As is well known in the industry, the contrast-limited adaptive histogram equalization (CLAHE) algorithm is used to improve local contrast and enhance edge sharpness in each region of a current image. In one embodiment, the histogram equalization module 410 operates based on the CLAHE algorithm and includes a tile generation module 411, a histogram equalization pipeline module 412, and an interpolation module 413. Note that the tile generation module 411 and the histogram equalization pipeline module 412 operate based on a previous image, while the interpolation module 413 operates based on the current image. The tile generation module 411 applies all average values ​​μ... Y The image is divided into multiple patches of the same size (corresponding to all pixels of the previous image), such as 64×64 or 256×256. The histogram equalization pipeline module 412 first performs histogram equalization on each patch output by the patch generation module 411 using a preset clip limit, and then calculates a cumulative distribution function (CDF) and a tone mapping function for each patch of the previous image. This is based on the coordinates of the center pixel S(x,y) and the average value μ from the LWS module 210. Y The histogram equalization pipeline module 412 generates at most four tone mapping functions for at most four patches of the nearest center pixel S(x,y). Finally, the interpolation module 413 performs bilinear or linear interpolation on the at most four mapping values ​​to obtain an interpolated value Y. map Please note that the CLAHE algorithm used in the histogram equalization module 410 described above is merely an example and not a limitation of this invention. In actual implementation, other histogram equalization algorithms may be used, which also fall within the scope of this invention.

[0047] In one embodiment of the tone adjustment module 420, the following program code is provided to adjust the tone based on the interpolated value Y. map Average value μ Y and a user-defined mixing parameter Ω TM Calculate the blending value Y of the center pixel S(x,y). TM and brightness ratio value R TM :

[0048] Y TM =(1-Ω) TM )×μ Y +Ω TM ×Y map ; / / Parameter Ω TM The range is from 0 to 1

[0049] R TM =μ Y >0? (Y) TM / μ Y ):1; / / If μ Y >0, Y TM =(Y TM / μ Y Otherwise R TM =1

[0050] As is well known in the industry, traditional tone mapping is used to enhance contrast. However, if the original Y component S of the center pixel S(x,y) is utilized... Y When used as input to the TM module 220 for tone mapping, noise will be easily amplified. Instead, this invention uses the average value μ Y The output of the low-pass filter 310 is used as the input to the TM module 220 for tone mapping to reduce noise.

[0051] Figure 5 This is a block diagram showing an EE module 230 according to an embodiment of the present invention. (See reference) Figure 5 The EE module 230 of this invention includes an EE filter 510 and a subtractor 520. The EE module 230 is used to improve sharpness through edge detection. The EE filter 510 can be implemented using any known edge enhancement filter, and the known edge enhancement filter performs edge enhancement filtering on the Y component of the group of nine pixels to produce a gradient magnitude M. EE and an edge enhancement brightness value Y EE . Figure 5The schematic diagram of the EE filter 510 is merely an example and not a limitation of the invention. The EE filter 510 includes an N×N Laplacian filter 511, an N×N Sober filter 512, two multipliers 513-514, and an adder 515. As is well known in the art, the first derivative of an image can be used to measure the rate of change of pixel intensity, which is very helpful for edge detection because edges are locations in the image where intensity changes rapidly. The Sober filter is a gradient-based edge detector that uses a horizontal kernel and a vertical kernel to calculate gradient representations along the x-axis and y-axis, respectively, to identify regions resembling edges in the horizontal and vertical directions. The second derivative of the image is used to measure the rate of change of the first derivative, which is very helpful for edge detection because zero crossings (the points where the second derivative changes sign) often correspond to edges. The Laplace filter is an edge detector that uses only a single symmetric kernel to calculate the second derivative in one step.

[0052] Since N=3, the 3×3 Laplacian filter 511 (corresponding to the aforementioned 3×3 single local pane) performs convolution calculation with the Y components of the group of nine pixels using a 3×3 Laplacian kernel to obtain a second derivative value n1. The 3×3 Sopper filter 512 (corresponding to the aforementioned 3×3 single local pane) performs convolution calculation with the Y components of the group of nine pixels using a 3×3 horizontal kernel and a 3×3 vertical kernel respectively to obtain a horizontal gradient value Gx and a vertical gradient value Gy. Then, based on the horizontal gradient value Gx and the vertical gradient value Gy, the 3×3 Sopper filter 512 calculates the gradient magnitude using the following mathematical formula: The multiplier 514 will multiply the gradient magnitude M EE Multiplying by an input parameter λ to obtain a product value n2, multiplier 513 multiplies the second derivative value n1 by the product value n2 to obtain a product value n3. Adder 515 multiplies the original Y component S of the center pixel S(x,y) by... Y Add the product value n3 to obtain the edge enhancement brightness value Y of the center pixel S(x,y). EE Then, subtractor 520 subtracts the original Y component S. Y Subtract Y EE And obtain a difference δ of the center pixel S(x,y). EE Please note that in TM module 220 and EE module 230, only the Y component of the group of nine pixels is analyzed.

[0053] AP module 240 receives multiple user-defined parameters (Ω) LWS ΩEE Ω C Ω TM ) and the multiple parameters (σ) generated by its upstream modules 210-230. Y ,sat,Δ,μ,Y TM R TM M EE δ EE This is used to execute an operation pipeline / process. The operation pipeline is divided into three stages: a first stage (or beta stage), a second stage (or α stage), and a third stage (or a mixed stage). In the first stage (or beta stage) of AP module 240, the following program code is provided to determine the operation based on three threshold values ​​(TH). sat TH dev TH EE ), saturation index (sat), gradient value (M) EE and standard deviation σ Y Calculate all beta factors:

[0054] β dev =σ Y >TH dev ? 1:(σ Y / TH dev ); / / β dev It is a deviation factor and TH dev It is a critical deviation value

[0055] β EE =M EE >TH EE ? 1:(M EE / TH EE ); / / β EE It is an EE factor and TH EE It is an EE critical value

[0056] β LWS =1-(β) dev +β EE ) / 2; / / β LWS It is an LWS factor

[0057] β sat =sat>TH sat ? 1:sat / TH sat ; / / β sat It is a saturation factor and TH sat It is a saturation critical value; if sat > TH sat , then β sat =1, otherwise, β sat =sat / TH sat

[0058] In another embodiment, β sat The factor calculation is as follows:

[0059] β sat =sat>100? 0:sat>TH sat ? (100-sat) / (100-TH) sat ):sat / TH sat ; / / like

[0060] If sat>100, then β sat =0, otherwise, check if sat is greater than TH. sat If so, then β sat = (100-sat) / (100-TH) sat Otherwise, β sat =sat / TH sat

[0061] In the second phase (or α phase) of AP module 240, the following program code is provided to determine the β factor and the average value μ as described above. Y and four user-defined parameters (Ω) LWS Ω EE Ω C Ω TM ), calculate all weights α:

[0062] α LWS =1-Ω LWS ×β LWS ; / / α LWS Represents an LWS weight

[0063] α EE =Ω EE ×β dev ; / / α EE Represents the weight of an EE

[0064] gain=(μ Y ==0||μ Y ==255)? 1:R TM ;

[0065] α TM =(1-Ω) TM )+Ω TM ×gain; / / α TM Represents the chromaticity ratio

[0066] α C =α TM ×β sat ×Ω C ; / / α C Represents the chromaticity weight.

[0067] In another embodiment, the weight α C The calculation is as follows: α C =(1+(α) TM -1)×β sat )×Ω C The parameter gain is calculated as follows:

[0068] gain=(μ Y ==0||μ Y ==255)? 1:μ Y <128? R TM :(255-Y Tm ) / (255-μ Y ); / / like

[0069] (μ Y ==0||μ Y If μ = 255, then gain = 1; otherwise, determine μ. Y Is it less than 128? If so, then gain = R. TM Otherwise, gain = (255 - Y) TM ) / (255-μ Y ).

[0070] Among them, Ω TM A user-defined TM strength, ranging from 0 to 1; Ω LWS A user-defined LWS strength, ranging from 0 to 1; Ω EE A user-defined EE strength, ranging from 0 to ∞; Ω C A user-defined chromaticity intensity, ranging from 0 to ∞. On the other hand, α... LWS Can be considered as σ Y M EE and Ω LWS The function, i.e., α LWS =f(σ Y M EE ,Ω LWS );α EE Can be considered as σ Y and Ω EE The function, i.e., α EE =f(σ Y ,Ω EE );α C Can be regarded as sat, R TM Ω C and Ω TM The function, i.e., α C =f(sat,R) TM ,Ω C ,Ω TM ).

[0071] In the final stage (or mixing stage) of AP module 240, the following program code is provided to determine the parameters (Δ, μ, Y). TM δ EE Given all the aforementioned values ​​α, calculate the enhancement component Y'U'V' of the center pixel S(x,y):

[0072] Y′=Y TM +α LWS ×Δ Y +α EE ×δ EE / / Equation 1

[0073] U′=128+α C ×(μ U -128)+α LWS ×Δ U ;

[0074] V′=128+α C ×(μ V -128)+α LwS ×Δ V ;

[0075] As shown above, the reinforcing component Y' contains a TM portion (Y TM ), and a noise reduction section (α) LWS ×Δ Y ) and an edge-reinforcing portion (α) EE ×δ Ee The reinforcing component U' contains a chromaticity portion (α). C ×(μ U -128)) and a noise reduction component (α) LWS ×Δ U The reinforcing component V' contains a chromaticity portion (α). C ×(μ V -128)) and a noise reduction component (α) LWS ×Δ V The image enhancement device 200 of this invention simultaneously processes the current image using three different image enhancement techniques (i.e., noise reduction, edge enhancement, and local contrast enhancement). Specifically, it adjusts weights (such as α) by analyzing detailed information of the current image (e.g., deviations and edge detection). LWS ,α EE and α C This is done to smooth the image and enhance its edges. For example, referring to Equation 1 above, to smooth the current image, α... LWS It should approach 0 in order to remove Δ Y The effect, conversely, if α LWSIf the value approaches 1, it will restore the initial value of the Y component, thus failing to smooth the image. On the other hand, α EE This involves adding edge enhancement gain control, if α EE Approaching 0, there is no edge enhancement effect. After the brightness value (Y' component) of the center pixel S(x,y) is updated, the chroma must also be adjusted accordingly; otherwise, insufficient color saturation will result. Therefore, α C It is used to compensate for color saturation.

[0076] Finally, based on the coordinates of the center pixel S(x,y), the center pixel S(x,y) with enhancement components Y'U'V' is stored in the output buffer 260. Then, all pixels of the current image are processed in the same manner to produce the enhanced image stored in the output buffer 260.

[0077] The LWS module 210, EE module 230, TM module 220, and AP module 240 of this invention can be implemented in software, hardware, or a combination of software (or firmware) and hardware. An example of a simple solution is an FPGA or an ASIC. In one embodiment, the LWS module 210, EE module 230, TM module 220, and AP module 240 are implemented using a general-purpose processor and a program memory. The program memory stores a processor-executable program. When the general-purpose processor executes the processor-executable program, the general-purpose processor is configured to operate as follows: the LWS module 210, the EE module 230, the TM module 220, and the AP module 240.

[0078] The functions and methods of the LWS module 210, EE module 230, TM module 220, and AP module 240, as well as their respective components, can be implemented using software, hardware, firmware, or a combination thereof. Software / firmware is a program with multiple sets of instructions, which can be executed using one or more digital circuits, such as a central processing unit (CPU), microprocessor, digital signal processor (DSP), built-in controller, or intellectual property core. If implemented in software / firmware mode, the above functions can be stored in the form of instructions or code on a computer-readable medium. The computer-readable medium includes computer storage media, including any non-transitory medium, facilitating the transfer of a computer program from one location to another. A storage medium can be any usable media accessible by a computer. For example, not as a limitation of this invention, the computer-readable medium includes random access memory (RAM), read-only memory (ROM), electronically erasable programmable read-only memory (EEPROM), CD-ROM and DVD-ROM optical discs, flash memory devices, magnetic disk storage devices, or any other media capable of storing program code in the form of instructions or data structures, and any combination thereof shall fall within the scope of computer-readable media.

[0079] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the patent application of the present invention; any equivalent changes or modifications made without departing from the spirit disclosed in the present invention shall be included within the scope of the claims.

Claims

1. An image enhancement device, characterized in that, Include: N line buffers are used to receive N rows of pixels from an image, and to output N values ​​based on an N×N local pane. 2 N×N local panes are pixels, wherein the N×N local panes are swiping across the image; A local pane statistics circuit for the N 2 The values ​​of each pixel are subjected to a low-pass filter to produce a filtered value μ, and a difference Δ is calculated between the filtered value μ and the value of a center pixel, wherein the center pixel is located at the center point of the N×N local pane. A tone mapping circuit, based on the luminance component μ of the filter value μ. Y And the coordinates of the center pixel, to generate a mapping value Y. map ; An edge enhancement circuit for the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a luminance difference δ. EE ;as well as An adaptive processing circuit, based on the four output μs of the local pane statistics circuit, the tone mapping circuit, and the edge enhancement circuit. Y Δ Y Y map and δ EE To update the luminance component of the center pixel; Where, Δ Y The luminance component representing the difference Δ, and the local pane statistics circuit, the tone mapping circuit, and the edge enhancement circuit operate in parallel.

2. The apparatus according to claim 1, characterized in that, The adaptive processing circuit further updates the chromaticity components of the center pixel based on the chromaticity components of the filter value μ and the difference Δ.

3. The apparatus according to claim 2, characterized in that, The adaptive processing circuit updates the luminance and chromaticity components of the center pixel according to the following equation: Y ′ =Y TM +a LWS ×a Y +a EE ×d EE ; U ′ =128+a C ×(μ U -128)+a LWS ×D U ; V ′ =128+a C ×(μ V -128)+a LWS ×D V ; Where, Δ U and Δ V μ represents the U component and V component of the difference Δ, respectively. U and μ V These represent the U component and V component of the filtered value μ, respectively. Where, α LWS It concerns the degree of image smoothing, α EE It concerns the degree of image edge enhancement and α. C It is used to compensate for color saturation; and Among them, Y TM It is Y map and μ Y A mixture of.

4. The apparatus according to claim 3, characterized in that, The local pane statistical circuit further calculates a saturation index sat based on the chromaticity component of the filtered value μ, and also calculates a saturation index sat based on the N... 2 The luminance components of each pixel produce a standard deviation σ. Y The tone mapping circuit further calculates a scaling value R. TM Equal to (Y) map / μ Y ), and wherein the edge enhancement circuitry thereon is more effective for the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a gradient value M. EE .

5. The apparatus according to claim 4, characterized in that, The edge enhancement circuit includes: An N×N Sober filter is used to filter the M 2 The brightness components of each pixel are analyzed to perform edge detection, generating a horizontal gradient value and a vertical gradient value, wherein the horizontal gradient value and the vertical gradient value are related to the gradient magnitude M. EE Related.

6. The apparatus according to claim 4, characterized in that, α LWS For σ Y M EE and Ω LWS The function, and Ω LWS The range is from 0 to 1.

7. The apparatus according to claim 4, characterized in that, α EE For σ Y and Ω EE The function, and Ω EE The range is from 0 to ∞.

8. The apparatus according to claim 4, characterized in that, α C For sat, R TM Ω C and Ω TM The function, and Ω in it. TM The range is from 0 to 1, and Ω C The range is from 0 to ∞.

9. An image enhancement method, characterized in that, Include: Store N rows of pixels from an image using N line buffers, and output N values ​​based on an N×N local pane. 2 Pixels, wherein the N×N local panes are swiping across the image; In a local pane statistics module: For the N 2 The values ​​of each pixel are subjected to a low-pass filter to produce a filtered value μ; and Calculate a difference Δ between the filtered value μ and the value of a center pixel, wherein the center pixel is located at the center point of the N×N local pane; In the tone mapping module: According to the brightness component μ of the filter value μ Y And the coordinates of the center pixel, to generate a mapping value Y. map ; An edge enhancement module: For the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a luminance difference δ. EE ;as well as In an adaptive processing module: Based on the four outputs μ of the local pane statistics module, the tone mapping module, and the edge enhancement module Y Δ Y Y map and δ EE Update the luminance component of the center pixel; Wherein, Δ1 represents the brightness component of the difference Δ, and the local pane statistics module, the tone mapping module, and the edge enhancement module operate in parallel.

10. The method according to claim 9, characterized in that, The step of updating the luminance component of the center pixel further includes: Update the luminance component of the center pixel using the following equation: Y ′ =Y TM +a LWS ×D Y +a EE ×d EE ; Where, α LWS It concerns the degree of image smoothing and α. EE It concerns the degree of image edge enhancement; and Among them, Y TM It is Y map and μ Y A mixture of.

11. The method according to claim 10, characterized in that, It also includes: In the local pane statistics module: According to the N 2 The luminance components of each pixel produce a standard deviation σ. Y ;as well as Regarding the edge enhancement module: For the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a gradient value M. EE .

12. The method according to claim 11, characterized in that, The steps for performing edge enhancement filtering further include: For the N 2 The luminance components of each pixel are analyzed using an N×N Sopper filter to generate a horizontal gradient value and a vertical gradient value, wherein the horizontal gradient value and the vertical gradient value are related to the gradient magnitude M. EE Related.

13. The method according to claim 11, characterized in that, α LWS For σ Y M EE and Ω LWS The function, and Ω LWS The range is from 0 to 1.

14. The method according to claim 11, characterized in that, α EE For σ Y and Ω EE The function, and Ω EE The range is from 0 to ∞.

15. The method according to claim 9, characterized in that, It also includes: Regarding the adaptive processing module: The chromaticity components of the center pixel are updated based on the filter value μ and the chromaticity components of the difference Δ.

16. The method according to claim 15, characterized in that, The step of updating the chromaticity components of the center pixel further includes: The chromaticity components of the center pixel are updated according to the following equation: U ′ =128+a C ×(μ U -128)+a LWS ×D U ; V ′ =128+a C ×(μ V -128)+a LWS ×D V ; Where, Δ U and Δ V The U and V components, representing the difference Δ, and μ, respectively. U and μ V The U and V components of the filtered value μ are represented respectively; and Where, α LWS It concerns the degree of image smoothing and α. C It is used to compensate for color saturation.

17. The method according to claim 16, characterized in that, It also includes: In the local pane statistics module: Based on the chromaticity components of the filtered value μ, a saturation index sat is generated; and According to the N 2 The luminance components of each pixel produce a standard deviation σ. Y ; Regarding the tone mapping module: Calculate a ratio R TM Equal to (Y) map / μ Y );as well as Regarding the edge enhancement module: For the N 2 The luminance components of each pixel are subjected to edge enhancement filtering to generate a gradient value M. EE .

18. The method according to claim 17, characterized in that, The steps for performing edge enhancement filtering further include: For the N 2 The luminance components of each pixel are analyzed using an N×N Sopper filter to generate a horizontal gradient value and a vertical gradient value, wherein the horizontal gradient value and the vertical gradient value are related to the gradient magnitude M. EE Related.

19. The method according to claim 17, characterized in that, α LWS For σ Y M EE and Ω LWS The function, and Ω LWS The range is from 0 to 1.

20. The method according to claim 17, characterized in that, α C For sat, R TM Ω C and Ω TM The function, and Ω in it. TM The range is from 0 to 1, and Ω C The range is from 0 to ∞.

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