Edge-based sharpness intensity control circuit, image sensing device, and method of operating the same

Abstract

To further divide an edge region in detail by the directionality of the edge region with respect to pixel values output from a plurality of pixels to remove noise and improve the texture.SOLUTION: An edge-based sharpness strength control circuit 300 includes an edge determination unit 310 that determines an edge region having edge information and a flat region having flat information with respect to a region corresponding to pixel data output from a plurality of pixels, a step edge determination unit 320 that determines a step edge region having a first edge having directionality information according to the directionality of the edge region and a texture edge region having a second edge having no directionality information with respect to the edge region determined by the edge determination unit 310, and a noise removing unit 330 that removes noise of the step edge region and the texture edge region determined by the step edge determination unit 320 through different filters having different gains from each other.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to semiconductor devices, and more particularly to an edge-based sharpness intensity control circuit, an image sensing device, and a method of operating the same. [Background technology]

[0002] In recent years, the paradigm for computing environments has shifted to ubiquitous computing, which allows computer systems to be used anywhere, anytime, and this has led to a rapid increase in the use of portable electronic devices such as mobile phones, digital cameras, and notebook computers.

[0003] In particular, the rapid development of video equipment has accelerated the development of image capturing devices such as cameras and camcorders equipped with image sensors. These image capturing devices are capable of capturing images, recording them on a recording medium, and playing them back at any time, and the number of users of these devices is rapidly increasing. As a result, users' demands for performance and functionality are gradually increasing, and they are pursuing smaller, lighter, and lower power consumption devices as well as higher performance and multiple functions. Summary of the Invention [Problem to be solved by the invention]

[0004] Embodiments of the present invention may provide an edge-based sharpness intensity control circuit, an image sensing device, and an operating method thereof that further divide an edge region into a step edge region and a texture edge region according to the directionality of the edge region for pixel values ​​output from a plurality of pixels, thereby removing noise and improving texture.

[0005] The technical problems to be solved by the present invention are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from the following description. [Means for solving the problem]

[0006] The present invention provides an edge-based sharpness intensity control circuit, an image sensing device, and a method of operating the same.

[0007] An edge-based sharpness intensity control circuit according to an embodiment of the present invention may include an edge determination unit that determines an edge region having edge information and a flat region having flat information for a region corresponding to pixel data output from a plurality of pixels included in a pixel array; a step edge determination unit that determines a step edge region having a first edge having directional information and a texture edge region having a second edge not having directional information for the edge region determined by the edge determination unit according to a direction of the edge region; and a noise reduction unit that removes noise from the step edge region and the texture edge region determined by the step edge determination unit using different filters having different gains.

[0008] The edge determination unit may determine a region corresponding to pixel data as an edge region if a standard deviation of pixel values ​​of surrounding pixels is greater than a reference value, or may determine a region corresponding to pixel data as a flat region if the standard deviation of surrounding pixel values ​​is less than the reference value. The step edge determination unit may determine the edge region as a step edge region if an accumulated number of directional changes in the edge region is less than a predetermined value, or may determine the edge region as a texture edge region if the accumulated number of directional changes is greater than a predetermined value.

[0009] The step edge determination unit may determine the step edge region as a strong step edge region when a magnitude of a difference value of surrounding pixels in the step edge region is greater than a preset value, and may determine the step edge region as a weak step edge region when a magnitude of the difference value of the surrounding pixels is smaller than a preset value.

[0010] The step edge determination unit may determine the texture edge region as a strong texture edge region when a magnitude of a difference value of surrounding pixels in the texture edge region is greater than a predetermined value, and may determine the texture edge region as a weak texture edge region when a magnitude of the difference value of the surrounding pixels is smaller than the predetermined value.

[0011] The noise removal unit may remove noise from the texture edge region using a high-gain, high-frequency filter for the texture edge region, and may remove noise from the step edge region using a low-gain, high-frequency filter for the step edge region.

[0012] The low-gain high-frequency filter can improve sharpness by subtracting a blurring signal from the original image signal.

[0013] The blurring signal can be generated using an average value of surrounding pixels having a directionality that does not match the directionality of the step edge.

[0014] The directionality of the edge region may include at least one of a horizontal direction, a vertical direction, a left diagonal direction, and a right diagonal direction.

[0015] In another embodiment of the present invention, an image sensing device includes an image sensor having a plurality of pixels and an image signal processor that processes an output signal of the image sensor. An edge-based sharpness intensity control circuit may be implemented within one of the image sensor and the image signal processor. The edge-based sharpness intensity control circuit may include an edge determination unit that determines an area corresponding to pixel data output from a plurality of pixels included in a pixel array as an edge area if a standard deviation of pixel values ​​of surrounding pixels is greater than a reference value, and determines an area as a flat area if the standard deviation of the surrounding pixel values ​​is less than the reference value. The edge determination unit may determine an edge area determined by the edge determination unit as a step edge area if an accumulated number of directional changes is less than a predetermined value, and determines an edge area as a texture edge area if the accumulated number of directional changes is greater than the predetermined value. A noise reduction unit that removes noise from the step edge area and the texture edge area determined by the step edge determination unit using different filters having different gains.

[0016] In addition, a method for operating an image sensing device according to still another embodiment of the present invention may include an edge determination step of determining, for a region corresponding to pixel data output from a plurality of pixels included in a pixel array, that the region is an edge region if a standard deviation of pixel values ​​of surrounding pixels is greater than a reference value, and determining the region as a flat region if the standard deviation of the surrounding pixel values ​​is less than the reference value; a step edge determination step of determining, for the edge region determined in the edge determination step, that the region is a step edge region if an accumulated number of directional changes is less than a predetermined value, and determining the region as a texture edge region if the accumulated number of directional changes is greater than the predetermined value; and a noise removal step of removing noise from the step edge region and the texture edge region determined in the step edge determination step using different filters having different gains.

[0017] The effects that can be obtained in the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those having ordinary skill in the art to which the present invention pertains from the following description. [Effects of the Invention]

[0018] The edge-based sharpness intensity control circuit, image sensing device, and operating method thereof according to embodiments of the present invention can further divide edge regions into step edge regions and texture edge regions, thereby eliminating dot noise or expressing detail images more clearly, thereby improving the sharpness of image quality. [Brief explanation of the drawings]

[0019] [Figure 1] 1 shows a block diagram of an edge-based sharpness intensity control circuit according to an embodiment of the present invention; [Figure 2] 2 is a diagram illustrating the division of step edge and texture edge regions by the step edge determination unit shown in FIG. 1. FIG. [Figure 3] FIG. 10 is a diagram illustrating the directionality of an edge region. [Figure 4] 2A to 2C are diagrams illustrating a method for removing noise in the noise removal unit shown in FIG. 1. [Figure 5] FIG. 10 shows a block diagram of an image sensing device according to another embodiment of the present invention. [Figure 6] FIG. 10 shows a block diagram of an image sensing device according to yet another embodiment of the present invention. [Figure 7] 10 is a flowchart illustrating an operation of an image sensing apparatus according to yet another embodiment of the present invention. [Figure 8] 1 shows a block diagram illustrating an embodiment of a system to which an image sensing device according to an embodiment of the present invention is applied. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that in the following description, only the parts necessary for understanding the operation of the present invention will be described, and the description of the other parts will be omitted so as not to obscure the gist of the present invention.

[0021] Hereinafter, embodiments of the present invention will be described in more detail with reference to the drawings.

[0022] An edge-based sharpness intensity control circuit according to one embodiment of the present invention will be described with reference to FIGS.

[0023] FIG. 1 is a block diagram of an edge-based sharpness intensity control circuit according to an embodiment of the present invention, FIG. 2 is a diagram illustrating the classification of step edge and texture edge regions by the step edge determination unit shown in FIG. 1, FIG. 3 is a diagram illustrating the directionality of edge regions, and FIG. 4 is a diagram illustrating a method of removing noise by the noise removal unit shown in FIG. 1.

[0024] The sharpness intensity control circuit 300 according to an embodiment of the present invention shown in FIG. 1 may include an edge determination unit 310, a step edge determination unit 320, and a noise reduction unit 330.

[0025] The edge determination unit 310 can determine an edge area having edge information and a flat area having flat information for pixel values ​​output from a plurality of pixels included in the pixel array.

[0026] 2, the edge determination unit 310 may classify a region as an edge region if the standard deviation (STD) of pixel values ​​of surrounding pixels is greater than a reference value, and classify a region as a flat region if the standard deviation of the surrounding pixel values ​​is less than the reference value. In this case, the reference value may be determined as an average value of green pixels among the surrounding pixels. In another embodiment, the reference value may be designed to be variably changed according to a brightness index.

[0027] The step edge determination unit 320 may determine whether the edge area determined by the edge determination unit 310 is a step edge area having a first edge with directional information or a texture edge area having a second edge without directional information, depending on the directionality of the edge area. In this case, the step edge area represents an edge area with clear directionality, and the texture edge area represents an area without clear directionality, such as grass, a desk pattern, or a stone pattern.

[0028] For example, the step edge determination unit 320 may determine the edge region as a step edge region if the cumulative number of directional changes in the edge region is smaller than a preset value, and may classify the edge region as a texture edge region if the cumulative number of directional changes is greater than a preset value.

[0029] That is, as shown in FIG. 2, if the cumulative number of directional changes is "0", the edge region is classified as a step edge region, and if the cumulative number of directional changes is "5" or "3", the edge region can be classified as a texture edge region.

[0030] In addition, the step edge determination unit 320 may determine a step edge region as a strong step edge region if the magnitude of the difference value of the surrounding pixels in the step edge region is greater than a preset value, and may determine the step edge region as a weak step edge region if the magnitude of the difference value of the surrounding pixels is less than a preset value.

[0031] In addition, the step edge determination unit 320 may determine a texture edge region as a strong texture edge region if the magnitude of the difference value of the surrounding pixels in the texture edge region is greater than a preset value, and may determine the texture edge region as a weak texture edge region if the magnitude of the difference value of the surrounding pixels is less than the preset value.

[0032] That is, as shown in FIG. 2, when the strength of the directional change in the texture edge region is large, it can be exemplified as "7", and when the strength of the directional change in the texture edge region is small, it can be exemplified as "2".

[0033] In this case, as shown in FIG. 3, the directionality of the edge region can be classified into horizontal, vertical, diagonal left (DL), and diagonal right (DR).

[0034] To simplify the amount of calculation, the gradient in each direction can be calculated using the sum of absolute differences (Sum of Absolute Differences), and in FIG. 3, "DLdiff" represents the absolute difference in the left diagonal direction, "Vdiff" represents the absolute difference in the vertical direction, "DRdiff" represents the absolute difference in the right diagonal direction, and "Hdiff" represents the absolute difference in the horizontal direction.

[0035] Also, the horizontal gradient according to the directionality of the edge region can be named "H_Gra", the vertical gradient "V_Gra", the left diagonal gradient "DL_Gra", and the right diagonal gradient "DR_Gra".

[0036] For example, if the following equation 1 is satisfied, the direction of the edge region can be determined as the diagonal right direction DR. [Number 1] A&B&(C|D), A represents |DL_Gra-DR_Gra|>threshold, B represents |DL_Gra-DR_Gra|-|H_Gra-V_Gra|<reference value (Threshold), C represents Max(H_Gra, V_Gra, DL_Gra, DR_Gra)!=DR_Gra, D represents Min(H_Gra, V_Gra, DL_Gra, DR_Gra)==DR_Gra.

[0037] Also, if the following equation 2 is satisfied, the direction of the edge region can be determined as the diagonal left direction DL. [Number 2] A&B&(C1|D1), A represents |DL_Gra-DR_Gra|>threshold, B represents |DL_Gra-DR_Gra|-|H_Gra-V_Gra|<reference value (Threshold), C1 represents Max(H_Gra, V_Gra, DL_Gra, DR_Gra)!=DL_Gra, D1 represents Min(H_Gra, V_Gra, DL_Gra, DR_Gra)==DL_Gra.

[0038] Also, if the following Equation 3 is satisfied, the direction of the edge region can be determined to be horizontal. [Number 3] A1&E&D2, A1 represents |DL_Gra-DR_Gra|<threshold, E represents V_Gra-H_Gra>reference value (Threshold), D2 represents Min(H_Gra, V_Gra, DL_Gra, DR_Gra)==H_Gra.

[0039] Also, if the following equation 4 is satisfied, the direction of the edge region can be determined to be vertical. [Number 4] A1&E1&D3, A1 represents |DL_Gra-DR_Gra|<threshold, E1 represents H_Gra-V_Gra>threshold, D3 represents Min(H_Gra, V_Gra, DL_Gra, DR_Gra)==V_Gra.

[0040] The noise removal unit 330 can improve sharpness by removing noises from the step edge region and the texture edge region determined by the step edge determination unit 320 using filters having different gains.

[0041] More specifically, the noise removal unit 330 may remove noise from the texture edge region using a high-gain high-frequency filter for the texture edge region, and may remove noise from the step edge region using a low-gain high-frequency filter for the step edge region.

[0042] At this time, as shown in FIG. 4, the low-gain high-frequency filter subtracts a blurring signal from the original image signal to generate a sharpness component, thereby improving the sharpness intensity.

[0043] In this case, the blurring signal can be generated using the average value (avg) of surrounding pixels having a direction that does not match the direction of the step edge. For example, the blurring signal can be generated using the average value (avg) to match the horizontal line shown in Figure 4. This can remove dot noise around the edge step and express the image in more detail.

[0044] On the other hand, noise removal for flat regions is a well-known technique, and therefore a description thereof will be omitted.

[0045] FIG. 5 shows a block diagram of an image sensing device according to one embodiment of the present invention.

[0046] As shown in FIG. 5, the image sensing device 10 may include an image sensor 100 and an image signal processor (ISP) 400.

[0047] The image sensing device 10 may be implemented as a personal computer (PC) or a mobile computing device, such as a laptop computer, a mobile phone, a smartphone, a tablet PC, a personal digital assistant (PDA), an enterprise digital assistant (EDA), a digital still camera, a digital video camera, a portable multimedia player (PMP), a mobile internet device (MID), a wearable computer, an internet of things (IoT) device, or an internet of everything (IoE) device.

[0048] The image sensor 100 shown in FIG. 5 may include a pixel array 200 and an edge-based sharpness intensity control circuit 300.

[0049] The pixel array 200 may include a plurality of pixels, where pixel may refer to pixel data, and may have, but is not limited to, an RGB data format, a YUV data format, or a YCbCr data format.

[0050] The edge-based sharpness intensity control circuit 300 removes noise in step edge regions and texture edge regions using filters with different gains, and outputs an image with improved sharpness.

[0051] The detailed configuration and operation of the edge-based sharpness intensity control circuit 300 are substantially the same as or similar to the configuration and operation of the edge-based sharpness intensity control circuit 300 shown in Figures 1 to 4, so detailed description thereof will be omitted.

[0052] The image signal processor 400 is an embodiment of a processor and can be realized as an integrated circuit, a system on chip (SoC), or a mobile AP. The image signal processor 400 processes the output signal of the image sensor 100. That is, the edge-based sharpness intensity control circuit 300 provided in the image sensor 100 removes noise in step edge regions and texture edge regions using filters having different gains, thereby providing and processing an image output signal with improved sharpness.

[0053] In particular, the image signal processor 400 can process a Bayer pattern (BAYER) corresponding to pixel data to generate RGB image data. For example, the image signal processor 400 can process the Bayer pattern (BAYER) so that the image data (IDATA) can be displayed on a display, and transmit the processed image data to an interface.

[0054] In some embodiments, the image sensor 100 and the image signal processor 400 may each be implemented as a chip and may be implemented in a single package, such as a multi-chip package (MCP). In other embodiments, the image sensor 100 and the image signal processor 400 may be implemented in a single chip.

[0055] FIG. 6 shows a block diagram of an image sensing device according to another embodiment of the present invention.

[0056] As shown in FIG. 6, the image sensing device 10 may include an image sensor 100 and an image signal processor (ISP) 400.

[0057] Except for the fact that the edge-based sharpness intensity control circuit 300 is implemented in the image signal processor 400 rather than in the image sensor 100, the structure and operation of the image sensing device 10 of FIG. 6 are substantially the same as or similar to the structure and operation of the image sensing device 10 of FIG. 5, and therefore detailed description thereof will be omitted.

[0058] Hereinafter, the operation of the image sensing apparatus according to still another embodiment of the present invention will be described with reference to Fig. 7. Fig. 7 is a flowchart illustrating the operation of the image sensing apparatus according to still another embodiment of the present invention.

[0059] The operation of the image sensing device shown in FIG. 7 includes an edge region determination step (S710), a step edge region determination step (S720), a step edge region noise removal step (S730), and a texture edge region noise removal step (S740).

[0060] In the edge region determination step (S710), if the standard deviation of the pixel values ​​of the surrounding pixels with respect to the pixel values ​​output from the plurality of pixels included in the pixel array is greater than a reference value, the pixel is classified as an edge region, and if the standard deviation of the surrounding pixel values ​​is less than the reference value, the pixel is classified as a flat region.

[0061] In the step edge area determination step (S720), if the cumulative number of directional changes for the edge area determined in the edge area determination step (S710) is less than a preset value, the edge area is determined to be a step edge area, and if the cumulative number of directional changes is greater than a preset value, the edge area is determined to be a texture edge area.

[0062] In this case, if the magnitude of the difference value of the surrounding pixels in the step edge region is greater than a preset value, the step edge region can be classified as a strong step edge region, and if the magnitude of the difference value of the surrounding pixels is less than the preset value, the step edge region can be classified as a weak step edge region.

[0063] In addition, if the magnitude of the difference value of the surrounding pixels in the texture edge region is greater than a predetermined value, the texture edge region can be classified as a strong texture edge region, and if the magnitude of the difference value of the surrounding pixels is less than a predetermined value, the texture edge region can be classified as a weak texture edge region.

[0064] In the step edge region noise removal step (S730), noise can be removed from the step edge region determined in the step edge determination step (S720) using a low-gain, high-frequency filter.

[0065] At this time, the low-gain high-frequency filter can improve the sharpness intensity by subtracting a blurring signal from the original image signal.

[0066] In the texture region noise removal step (S740), noise can be removed from the texture edge region determined in the edge determination step (S720) using a high-gain, high-frequency filter.

[0067] An embodiment of a system to which an image sensing device according to an embodiment of the present invention is applied will be described below. Fig. 8 is a block diagram illustrating an embodiment of a system to which an image sensing device according to an embodiment of the present invention is applied.

[0068] As shown in FIG. 8, the system shown in FIG. 8 can be any of a variety of types of computing devices, including, but not limited to, a personal computer system, a desktop computer, a laptop or notebook computer, a mainframe computer system, a handheld computing device, a cellular phone, a smartphone, a mobile phone, a workstation, a network computer, a consumer device, an application server, a storage device, an intelligent display, a peripheral device such as a switch, modem, router, etc., or generally any type of computing device.

[0069] According to one embodiment, the system illustrated in FIG. 8 may represent a system-on-a-chip (SOC). As the name implies, the components of the SOC (1000) may be integrated onto a single semiconductor substrate, such as an integrated circuit "chip." In some embodiments, the components may be implemented on two or more separate chips in the system. The SOC (1000) will be used herein as an example.

[0070] In the illustrated embodiment, the components of the SOC (1000) may include a central processing unit (CPU) complex 1020, on-chip peripheral components 1040A and 1040B (more simply, "peripherals"), a memory controller (MC) 1030, an image signal processor 400, and a communications fabric 1010.

[0071] The SOC (1000) may be further coupled to additional components such as memory 1800 and image sensor 100. All of the components (1020, 1030, 1040A and 1040B, and 200) may be coupled to a communications fabric 1010. The memory controller 1030 may be coupled to memory 1800 during use, and the peripheral device 1040B may be coupled to an external interface 1900 during use.

[0072] In the illustrated embodiment, CPU complex 1020 may include one or more processors 1024 and a level 2 (L2) cache 1022. Peripherals 1040A and 1040B may be any set of additional hardware functionality included in SOC 1000. For example, peripherals 1040A and 1040B may include a display controller configured to display video data on one or more display devices, a graphics processing unit (GPU), a video encoder / decoder, a scaler, a rotator, a blender, etc.

[0073] The image signal processor 400 can process image capture data from the image sensor 100 (or other image sensors). The image signal processor 400 and the image sensor 100 may have the same configurations and operations as the image signal processor 400 and the image sensor 100 shown in FIGS. 1 to 7.

[0074] The peripherals may further include audio peripherals, such as microphones, speakers, interfaces to microphones and speakers, audio processors, digital signal processors, mixers, etc. The peripherals may include a peripheral interface controller (e.g., peripheral 1040B) for various interfaces 1900 external to the SOC (1000), including interfaces such as Universal Serial Bus (USB), Peripheral Component Interconnect (PCI) including PCI Express (PCIe), serial and parallel ports, etc. The peripherals may further include networking peripherals, such as a Media Access Controller (MAC). In general, any set of hardware may be included according to various embodiments.

[0075] The CPU complex 1020 may include one or more CPU processors 1024 that act as the CPU for the SOC (1000). The CPU of a system may include a processor (or processors) that executes the main control software of the system, e.g., an operating system. Generally, during use, the software executed by the CPU may control other components of the system to achieve the intended system functionality. The processor 1024 may also execute other software, e.g., application programs. The application programs may provide user functionality and may rely on the operating system for low-level device control. Thus, the processor 1024 may also be referred to as an application processor.

[0076] CPU complex 1020 may further include interfaces to other hardware, such as an L2 cache 1022 and / or other components of the system (eg, an interface to communications fabric 1010).

[0077] Generally, a processor may include any circuitry and / or microcode configured to execute instructions defined in an instruction set architecture implemented by the processor. Instructions and data operated on by the processor in response to executing instructions may generally be stored in memory 1800, although certain instructions may also be defined for direct processor access to peripheral devices, etc. A processor may encompass a processor core implemented on an integrated circuit with other components, etc., as an integrated part, such as a system-on-chip (SOC) (1000) or other level. A processor may further encompass another microprocessor, processor core, and / or a microprocessor integrated within a multi-chip module implementation, a processor implemented as multiple integrated circuits, etc.

[0078] The memory controller 1030 may generally include circuitry for receiving memory operations from other components of the SOC (1000) and for accessing and performing memory operations on the memory 1800. The memory controller 1030 may be configured to access any type of memory 1800. For example, the memory 1800 may be static random access memory (SRAM), dynamic RAM (DRAM), or synchronous DRAM (SDRAM), including double data rate (DDR, DDR2, DDR3, etc.) DRAM. Low-power / mobile versions of DDR DRAM (e.g., LPDDR, mDDR, etc.) may also be supported. The memory controller 1030 may include queues for memory operations, directing (and potentially redirecting) operations and submitting operations to the memory 1800. The memory controller 1030 may further include data buffers for storing write data awaiting writing to memory and read data awaiting return to the source of the memory operation.

[0079] In some embodiments, the memory controller 1030 may include a memory cache that stores recently accessed memory data. In a SOC implementation, for example, the memory cache may reduce power costs in the SOC by avoiding re-accessing data from memory 1800 if it is expected to be accessed again soon. In some cases, the memory cache may also be referred to as a system cache, as opposed to a private cache that only serves certain components, such as the L2 cache 1022 or cache of the processor 1024. Additionally, in some embodiments, the system cache need not be located within the memory controller 1030.

[0080] In embodiments, memory 1800 can be packaged with SOC (1000) in a chip-on-chip or package-on-package configuration. A multi-chip module configuration of SOC (1000) and memory 1800 can also be used. Such a configuration can be relatively more stable (in terms of data observation) than transmission to other components in the system (e.g., to endpoints 16A and 16B). Thus, protected data can reside unencrypted in memory 1800, while the protected data can be encrypted for exchange between SOC (1000) and external endpoints.

[0081] The communications fabric 1010 can be any communications interconnect and protocol for communication among the components of the SOC (1000). The communications fabric 1010 can be bus-based, including shared bus configurations, crossbar configurations, and hierarchical buses with bridges. The communications fabric 1010 can also be packet-based, hierarchical with bridges, crossbar, point-to-point, or other interconnects. There may be more or fewer individual components / subcomponents than those shown in FIG. 9.

[0082] In some embodiments, the methods described herein can be implemented by a computer program product, or software. In some embodiments, a non-transitory, computer-readable storage medium can store instructions that can be used to program a computer system (or other electronic device) to perform some or all of the techniques described herein. A computer-readable storage medium can include any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). Machine-readable media can include, but are not limited to, magnetic storage media (e.g., floppy diskettes), optical storage media (e.g., CD-ROMs), magneto-optical storage media, read-only memory (ROM), random access memory (RAM), erasable and programmable memory (e.g., EPROMs and EEPROMs), flash memory, and other types of media suitable for storing electrical or program instructions. Additionally, program instructions can be communicated using optical, acoustic, or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.).

[0083] The computer system 1000 may include a processor unit 1020 (possibly including multiple processors, single-threaded processors, multi-threaded processors, multi-core processors, etc.) that may be configured to execute one or more modules, e.g., noise reduction circuits, that may reside within program instructions stored in memory 1800 of the same computer system, or that may reside within program instructions stored in the memory of yet another computer system similar to or different from the computer system 1000.

[0084] While specific embodiments have been described in the detailed description of the present invention, it is of course possible to make various modifications without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the following claims as well as equivalents to the claims. [Explanation of symbols]

[0085] 10 Image sensing device 100 image sensors 200 pixel array 300 Edge-based sharpness intensity control circuit 310 Edge determination unit 320 Step edge judgement unit 330 Noise removal section 400 Image Signal Processor

Claims

1. an edge determination unit for determining an edge area having edge information and a flat area having flat information with respect to an area corresponding to pixel data output from a plurality of pixels included in the pixel array; a step edge determination unit that determines, based on the directionality of the edge area determined by the edge determination unit, a step edge area having a first edge with directional information and a texture edge area having a second edge without directional information; a noise removal unit that removes noise from the step edge region and the texture edge region determined by the step edge determination unit using different filters having different gains; Equipped with The step edge determination unit is an edge-based sharpness intensity control circuit that further classifies the edge region into a step edge region and a texture edge region based on the cumulative number of directional changes in the edge region.

2. the edge determination unit determines a region corresponding to the pixel data as an edge region if a standard deviation of pixel values ​​of the surrounding pixels is greater than a reference value, and determines a region corresponding to the pixel data as a flat region if the standard deviation of the surrounding pixel values ​​is less than the reference value; 2. The edge-based sharpness intensity control circuit of claim 1, wherein the step edge determination unit determines the edge region as a step edge region when an accumulated number of directional changes in the edge region is smaller than a preset value, and determines the edge region as a texture edge region when the accumulated number of directional changes is greater than a preset value.

3. The step edge determination unit 3. The edge-based sharpness intensity control circuit of claim 2, wherein if the magnitude of the difference value of the surrounding pixels in the step edge region is greater than a predetermined value, the step edge region is determined to be a strong step edge region, and if the magnitude of the difference value of the surrounding pixels is less than the predetermined value, the step edge region is determined to be a weak step edge region.

4. The step edge determination unit 3. The edge-based sharpness intensity control circuit of claim 2, wherein the texture edge region is determined to be a strong texture edge region when a magnitude of a difference value of surrounding pixels in the texture edge region is greater than a predetermined value, and the texture edge region is determined to be a weak texture edge region when a magnitude of the difference value of the surrounding pixels is less than the predetermined value.

5. 2. The edge-based sharpness intensity control circuit of claim 1, wherein the noise removal unit removes noise from the texture edge region using a high-gain, high-frequency filter for the texture edge region, and removes noise from the step edge region using a low-gain, high-frequency filter for the step edge region.

6. 6. The edge-based sharpness intensity control circuit of claim 5, wherein the low-gain high-frequency filter improves sharpness intensity by subtracting a blurring signal from an original image signal.

7. The edge-based sharpness intensity control circuit of claim 6 , wherein the blurring signal is generated using an average value of surrounding pixels having a directionality that does not match the directionality of the step edge region.

8. The edge-based sharpness intensity control circuit of claim 1 , wherein the directionality of the edge region includes at least one of a horizontal direction, a vertical direction, a left diagonal direction, and a right diagonal direction.

9. an image sensor having a plurality of pixels; an image signal processor for processing an output signal of the image sensor; Equipped with an edge-based sharpness intensity control circuit is implemented within one of the image sensor and the image signal processor; The edge-based sharpness intensity control circuit includes: an edge determination unit for determining an edge area having edge information and a flat area having flat information with respect to an area corresponding to pixel data output from a plurality of pixels included in the pixel array; a step edge determination unit that determines, based on the directionality of the edge area determined by the edge determination unit, a step edge area having a first edge with directional information and a texture edge area having a second edge without directional information; a noise removal unit that removes noise from the step edge region and the texture edge region determined by the step edge determination unit using different filters having different gains; Equipped with The step edge determination unit further classifies the edge region into a step edge region and a texture edge region based on an accumulated number of directional changes in the edge region.

10. the edge determination unit determines a region corresponding to the pixel data as an edge region if a standard deviation of pixel values ​​of the surrounding pixels is greater than a reference value, and determines a region corresponding to the pixel data as a flat region if the standard deviation of the surrounding pixel values ​​is less than the reference value; 10. The image sensing device of claim 9, wherein the step edge determination unit determines the edge region as a step edge region when an accumulated number of directional changes in the edge region is smaller than a preset value, and determines the edge region as a texture edge region when the accumulated number of directional changes is greater than a preset value.

11. The step edge determination unit 10. The image sensing device of claim 9, wherein the step edge region is determined to be a strong step edge region when a magnitude of a difference value of a neighboring pixel in the step edge region is greater than a predetermined value, and the step edge region is determined to be a weak step edge region when a magnitude of the difference value of the neighboring pixel is smaller than the predetermined value.

12. The step edge determination unit 10. The image sensing device of claim 9, wherein the texture edge region is determined to be a strong texture edge region when a magnitude of a difference value of surrounding pixels in the texture edge region is greater than a predetermined value, and the texture edge region is determined to be a weak texture edge region when a magnitude of the difference value of the surrounding pixels is smaller than the predetermined value.

13. 10. The image sensing device of claim 9, wherein the noise removal unit removes noise from the texture edge region using a high-gain, high-frequency filter for the texture edge region, and removes noise from the step edge region using a low-gain, high-frequency filter for the step edge region.

14. The image sensing device of claim 13, wherein the low-gain high-frequency filter improves sharpness by subtracting a blurring signal from the original image signal.

15. 15. The image sensing device of claim 14, wherein the directionality of the edge region includes a horizontal direction, a vertical direction, a left diagonal direction, and a right diagonal direction, and the blurring signal is generated using an average value of surrounding pixels having a direction that does not match the directionality of the step edge region.

16. an edge determining step of determining an edge area having edge information and a flat area having flat information for an area corresponding to pixel data output from a plurality of pixels included in the pixel array; a step edge determination step of determining a step edge area having a first edge with directional information and a texture edge area having a second edge without directional information according to the directionality of the edge area determined in the edge determination step; and a noise removal step of removing noise from the step edge area and the texture edge area determined in the step edge determination step using different filters having different gains. Including, The step edge determining step further classifies the edge region into a step edge region and a texture edge region based on an accumulated number of directional changes in the edge region.

17. The edge determination step determines the area corresponding to the pixel data as an edge area if the standard deviation of the pixel values ​​of the surrounding pixels is greater than a reference value, and determines the area corresponding to the pixel data as a flat area if the standard deviation of the pixel values ​​of the surrounding pixels is less than the reference value; 17. The method of claim 16, wherein the step edge determination step determines the edge region as a step edge region if an accumulated number of directional changes in the edge region is smaller than a preset value, and determines the edge region as a texture edge region if the accumulated number of directional changes is greater than a preset value.

18. In the step edge determination step, 17. The method of claim 16, wherein the step edge region is determined to be a strong step edge region when a magnitude of a difference value of a neighboring pixel in the step edge region is greater than a predetermined value, and the step edge region is determined to be a weak step edge region when a magnitude of the difference value of the neighboring pixel is less than the predetermined value.

19. In the step edge determination step, 17. The method of claim 16, wherein the texture edge region is determined to be a strong texture edge region when a magnitude of a difference value of surrounding pixels in the texture edge region is greater than a predetermined value, and the texture edge region is determined to be a weak texture edge region when a magnitude of the difference value of the surrounding pixels is less than the predetermined value.

20. 17. The method of claim 16, wherein the noise removal step removes noise from the texture edge region using a high-gain, high-frequency filter for the texture edge region and removes noise from the step edge region using a low-gain, high-frequency filter for the step edge region.

21. 21. The method of claim 20, wherein the low-gain high-frequency filter subtracts a blurring signal from the original signal to improve sharpness intensity.

22. 22. The method of claim 21, wherein the directionality of the edge region includes at least one of a horizontal direction, a vertical direction, a left diagonal direction, and a right diagonal direction, and the blurring signal is generated using an average value of surrounding pixels having a direction that does not match the directionality of the step edge region.

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