Image sensing device and method of operating the same

Abstract

An image sensing device and an operation method thereof are disclosed. The image sensing device may include: an analyzer adapted to analyze a state of each of a plurality of kernels based on system information and a plurality of pixel values; a detector adapted to detect color noise of a target pixel value among pixel values ​​included in a target kernel among the plurality of kernels according to an analysis result of the analyzer; and a corrector adapted to correct the target pixel value according to a detection result of the detector.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Korean Patent Application No. 10-2021-0052598, filed on April 22, 2021, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] Various embodiments of the present disclosure relate to semiconductor design technology, and more particularly, to an image sensing device and an operating method thereof. Background Art

[0004] An image sensing device is a device for capturing images using the property of semiconductors to react to light. Image sensing devices are generally classified into charge coupled device (CCD) image sensing devices and complementary metal oxide semiconductor (CMOS) image sensing devices. Recently, CMOS image sensing devices are widely used because CMOS image sensing devices can allow both analog control circuits and digital control circuits to be directly implemented on a single integrated circuit (IC). Summary of the invention

[0005] Various embodiments of the present disclosure are directed to an image sensing device supporting a denoising technique for low-illumination images.

[0006] According to an embodiment of the present disclosure, an image sensing device may include: an analyzer adapted to analyze the state of each of a plurality of kernels based on system information and a plurality of pixel values; a detector adapted to detect color noise of a target pixel value among pixel values ​​included in a target kernel among the plurality of kernels according to an analysis result of the analyzer; and a corrector adapted to correct the target pixel value according to the detection result of the detector.

[0007] According to an embodiment, an image sensing device may include: an image sensor including a pixel array in which first pixels, second pixels, and third pixels are arranged in a predetermined color pattern, each of the first pixels has a first color filter, each of the second pixels has a second color filter, and each of the third pixels has a third color filter, and the above-mentioned image sensor is suitable for generating multiple pixel values ​​corresponding to the color pattern; and an image processor, which is suitable for detecting color noise of each of multiple kernels based on system information and multiple pixel values, and detecting color noise of a target pixel value included in a target kernel among the multiple kernels by using all pixel values ​​included in the target kernel.

[0008] According to an embodiment of the present disclosure, an operating method of an image sensing device may include: determining the saturation and texture of each of a plurality of kernels; when a target kernel among the plurality of kernels is a low-saturation and flat area, detecting color noise of a target pixel value among pixel values ​​included in the target kernel; and correcting the color noise of the target pixel value.

[0009] According to an embodiment, an image sensing device may include: an image sensor, which includes a pixel array suitable for generating multiple pixel values ​​corresponding to a set frame; and an image processor, which is suitable for: determining whether the above-mentioned frame has low illumination; when it is determined that the above-mentioned frame has low illumination, selecting the pixel value corresponding to the target kernel in the above-mentioned frame; determining whether the target kernel is a flat area and a low-saturation area; when it is determined that the target kernel is a flat area and a low-saturation area, based on other pixel values ​​of the target kernel having other color channels different from the color channel of the target pixel value of the target kernel, detecting whether the target pixel value corresponds to color noise; and when it is detected that the target kernel corresponds to color noise, using the other pixel values ​​to correct the target pixel value. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a block diagram illustrating an image sensing device according to an embodiment of the present disclosure.

[0011] Figure 2 The present invention is a diagram showing an embodiment of the present invention. Figure 1 Block diagram of the image sensor shown.

[0012] Figure 3 The present invention is a diagram showing an embodiment of the present invention. Figure 2 A configuration diagram of an example of a pixel array is shown.

[0013] Figure 4 The present invention is a diagram showing an embodiment of the present invention. Figure 1 A block diagram of the image processor is shown.

[0014] Figure 5 The present invention is a diagram showing an embodiment of the present invention. Figure 4 A block diagram of the analyzer is shown.

[0015] Figure 6 The present invention is a diagram showing an embodiment of the present invention. Figure 1 Flowchart of the operation of the image sensing device shown.

[0016] Figure 7 The present invention is a diagram showing an embodiment of the present invention. Figure 5 A diagram illustrating the operation of the second determination component is shown.

[0017] Figure 8 The present invention is a diagram showing an embodiment of the present invention. Figure 5 A diagram illustrating the operation of the third determination component is shown.

[0018] Fig. 9 The present invention is a diagram showing an embodiment of the present invention. Figure 4 A diagram showing the operation of the detector.

[0019] Fig.10 The present invention is a diagram showing an embodiment of the present invention. Figure 4 A diagram showing the operation of the corrector.

[0020] Fig.11 is a graph showing denoising performance according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] Various embodiments of the present disclosure are described below in conjunction with the accompanying drawings to describe the present invention in detail so that a person skilled in the art can easily implement the technical spirit of the present disclosure.

[0022] It will be understood that when an element is referred to as being "connected to" or "coupled to" another element, the element may be directly connected to or directly coupled to the other element, or electrically connected to or electrically coupled to the other element with one or more elements interposed therebetween. In addition, it will be understood that when used in this specification, the terms "comprises," "comprising," "including," and "comprising" do not exclude the presence of one or more other elements, but may also include or have one or more other elements, unless otherwise mentioned. In the description throughout the specification, certain components are described in the singular, but the present disclosure is not limited thereto, and it will be understood that the components may be formed in the plural.

[0023] Figure 1 is a block diagram illustrating an image sensing device 10 according to an embodiment of the present disclosure.

[0024] refer to Figure 1 , the image sensing device 10 may include an image sensor 100 and an image processor 200 .

[0025] The image sensor 100 may generate a plurality of pixel values ​​DPX corresponding to the captured image. That is, the image sensor 100 may generate a plurality of pixel values ​​DPX corresponding to a single frame.

[0026] The image processor 200 may detect and correct color noise of each kernel based on the system information AG and the plurality of pixel values ​​DPX. In particular, the image processor 200 may detect color noise of a target pixel value included in a target kernel by using all pixel values ​​included in the target kernel.

[0027] Figure 2FIG. 2 is a diagram showing an image sensor (eg, Figure 1 1. A block diagram of an image sensor 100 is shown.

[0028] refer to Figure 2 , the image sensor 100 may include a pixel array 110 and a signal converter 120 .

[0029] The pixel array 110 may include a plurality of pixels arranged in row and column directions (refer to Figure 3 ). The pixel array 110 may generate a plurality of pixel signals VPX for each row. For example, the pixel array 110 may generate a plurality of pixel signals VPX from pixels arranged in a first row during a first row time, and may generate a plurality of pixel signals VPX from pixels arranged in an nth row during an nth row time, where “n” is an integer greater than 2. Each pixel signal VPX may be an analog pixel signal.

[0030] The signal converter 120 may convert the analog pixel signal VPX into a digital pixel value DPX. The digital pixel value DPX may be output corresponding to a Bayer pattern described below. For example, the signal converter 120 may include an analog-to-digital converter.

[0031] Figure 3 FIG. 2 is a diagram showing a pixel array (eg, Figure 2 1 is a configuration diagram of an example of a pixel array 110).

[0032] refer to Figure 3 , the pixel array 110 may be arranged in a predetermined pattern. For example, the pixel array 110 may be arranged in a Bayer pattern. The Bayer pattern may be composed of repeated units each having 2x2 pixels. In each unit, two pixels Gb and Gr each having a green filter may be arranged to be obliquely opposite to each other at a corner of the unit, and a pixel B having a blue filter and a pixel R having a red filter may be arranged at other corners of the unit. The four pixels B, Gb, Gr, and R are not necessarily limited to Figure 3 Instead of the arrangement structure shown in , it can be arranged differently based on the Bayer pattern as described above. Although the embodiment describes the Bayer pattern as an example, the present disclosure is not necessarily limited thereto, but can be applied to various patterns such as a Quad pattern.

[0033] The pixel array 110 may be divided into "nxn" pixels, where "n" is a natural number greater than or equal to 2. Hereinafter, an example in which pixel values ​​corresponding to 8x8 pixels are included in one kernel is described.

[0034] Figure 4 FIG. 2 is a diagram showing an image processor (eg, Figure 1 A block diagram of an image processor 200 is shown.

[0035] refer to Figure 4 , the image processor 200 may include an analyzer 210 , a detector 220 , and a corrector 230 .

[0036] The analyzer 210 may analyze the state of each kernel based on the system information AG and the plurality of pixel values ​​DPX. The system information AG may include an analog gain value. The state may be related to the illumination of the target kernel, the saturation of the target kernel, and the texture of the target kernel.

[0037] The detector 220 may detect color noise of a target pixel value among pixel values ​​included in the target kernel based on the analysis results RES1 / 2 / 3 of the analyzer 210 and a plurality of pixel values ​​DPX. For example, the detector 220 may detect color noise of the target pixel value based on pixel values ​​belonging to a color channel different from a color channel to which the target pixel value belongs among pixel values ​​included in the target kernel. The detector 220 may perform an operation of detecting color noise when the illumination of the target kernel, the saturation of the target kernel, and / or the texture of the target kernel satisfy one or more predetermined conditions.

[0038] The corrector 230 may correct the target pixel value based on the detection result DET of the detector 220 and the plurality of pixel values ​​DPX. For example, the corrector 230 may correct the color noise based on the pixel values ​​belonging to a color channel different from the color channel to which the target pixel value belongs, among the pixel values ​​included in the target kernel.

[0039] Figure 5 is a diagram showing an analyzer (eg, Figure 4 A block diagram of the analyzer 210) is shown.

[0040] refer to Figure 5 , the analyzer 210 may include a first determining component 211, a second determining component 213, and a third determining component 215. The first determining component 211, the second determining component 213, and the third determining component 215 include all circuits, systems, software, firmware, and devices necessary for their respective operations and functions.

[0041] The first determining part 211 may determine whether a single frame corresponding to a plurality of pixel values ​​DPX has low illumination based on the system information AG. For example, when the analog gain value is greater than or equal to a predetermined value, the first determining part 211 may determine that the current frame is in a low illumination state. Typically, under low illumination conditions, the analog gain value may have a relatively large value. The first determining part 211 may output a first determination signal RES1 to the detector 220 as an analysis result related to low illumination.

[0042] The second determining part 213 may determine whether the texture is a flat area for each kernel based on a plurality of pixel values ​​DPX. For example, the second determining part 213 may determine whether the texture of the target kernel is a flat area based on the pixel values ​​belonging to the green channel among the pixel values ​​included in the target kernel. The second determining part 213 may output a second determination signal RES2 to the detector 220 as an analysis result related to the texture.

[0043] The third determining part 215 can determine whether each kernel is a low saturation area based on a plurality of pixel values ​​DPX. For example, the third determining part 215 can determine whether the target kernel is a low saturation area based on the first average value of the pixel values ​​of the first color channel identical to the color channel to which the target pixel value belongs among the pixel values ​​included in the target kernel, the second average value of the pixel values ​​of the second color channel among the pixel values ​​included in the target kernel, and the third average value of the pixel values ​​of the third color channel among the pixel values ​​included in the target kernel.

[0044] In the following, reference Figures 6 to 11 The operation of the image sensing device 10 having the above-described configuration according to the embodiment will be described.

[0045] Figure 6 FIG. 2 is a diagram showing an image sensing device (eg, Figure 1 Flowchart of the operation of the image sensing device 10) shown.

[0046] refer to Figure 6 , in operation S101, the image sensor 100 may generate a plurality of pixel values ​​DPX corresponding to a single frame.

[0047] In operation S103, the image processor 200 may determine whether a single frame has low illumination. For example, when the analog gain value is greater than or equal to a predetermined value, the first determining part 211 may determine that the current frame is in a low illumination state. Typically, under low illumination conditions, the analog gain value may have a relatively large value.

[0048] When it is determined that a single frame has low illumination (i.e., "yes" in S103), the image processor 200 can determine the saturation and texture of each kernel. First, in operation S105, the image processor 200 can select a target kernel. In some embodiments, the target kernel may include 8x8 pixel values, taking the target pixel value as a reference. The target pixel value may refer to the pixel value of the pixel set at the center among the pixel values ​​of the 8x8 pixels. Next, in operation S107, the image processor 200 may determine whether the target kernel is a flat area. For example, the second determining component 213 may determine whether the texture of the target kernel is a flat area based on the pixel value belonging to the green channel among the pixel values ​​included in the target kernel. When it is determined that the texture of the target kernel is a flat area (i.e., "yes" in S107), in operation S109, the image processor 200 may determine whether the target kernel is a low saturation area. For example, the third determination component 215 can determine whether the target kernel is a low-saturation area based on a first average value of pixel values ​​among the pixel values ​​included in the target kernel and belonging to the same color channel as the target pixel value, a second average value of pixel values ​​among the pixel values ​​included in the target kernel and belonging to the second color channel, and a third average value of pixel values ​​among the pixel values ​​included in the target kernel and belonging to the third color channel.

[0049] When it is determined that the target kernel is a low saturation area (i.e., "yes" in S109), in operation S111, the image processor 200 may detect whether the target pixel value included in the target kernel is color noise. For example, the detector 220 may detect the color noise of the target pixel value based on the pixel value of the color channel different from the color channel to which the target pixel value belongs among the pixel values ​​included in the target kernel. As described above, the detector 220 may perform the operation of detecting color noise only when the state of the target kernel satisfies a predetermined condition, that is, only when the target kernel has low illumination and is a low saturation area and the texture of the target kernel is a flat area.

[0050] When it is determined that color noise is detected (i.e., "Yes" in S113), in operation S115, the image processor 200 may correct the color noise. For example, the corrector 230 may correct the color noise based on pixel values ​​belonging to color channels different from the color channel to which the target pixel value belongs (i.e., the second color channel and the third color channel) among the pixel values ​​included in the target kernel.

[0051] Operations S105 to S115 described above may be repeated until it is determined in operation S117 that each kernel included in the single frame is selected as a target kernel.

[0052] Figure 7is a diagram showing the operation of the second determination component according to an embodiment of the present disclosure (eg, Figure 5 FIG. 214 shows a diagram of a second determining component 213).

[0053] refer to Figure 7 , the second determining part 213 can calculate the standard deviation value STDG based on the pixel values ​​P01, P03, P05, P07, P10, P12, P14, P16, P18, P21, P23, P25, P27, P30, P32, P34, P36, P38, P41, P43, P45, P47, P50, P52, P54, P56, P58, P61, P63, P65, P67, P70, P72, P74, P76, P78, P81, P83, P85 and P87 belonging to the green channel included in the target kernel. The standard deviation value STDG can be defined by the following equation 1.

[0054] [Equation 1]

[0055] STDG=|gain / 4*{∑|Gpix-Gavg|}±offset

[0056] Herein, “Gpix” may refer to each pixel value belonging to the green channel, “Gavg” may refer to an average value AVG2 of pixel values ​​P01 to P87 belonging to the green channel, and “Gain” and “offset” may refer to variables scaled corresponding to the fourth threshold TV4.

[0057] When the standard deviation value STDG is less than the fourth threshold TV4, the second determination part 213 may determine that the target kernel is a flat area. For reference, the fourth threshold TV4 may be a value adaptively adjusted according to the average value AVG2 of the pixel values ​​P01 to P87 belonging to the green channel.

[0058] Figure 8 is a diagram showing a third determining component (eg, Figure 5 A diagram of the operation of the third determining component 215) shown.

[0059] refer to Figure 8, based on the first average value AVG1 of the pixel values ​​P00, P02, P04, P06, P08, P20, P22, P24, P26, P28, P40, P42, P44, P46, P48, P60, P62, P64, P66, P68, P80, P82, P84, P86 and P88 belonging to the blue channel to which the target pixel value P44 belongs, and the pixel values ​​P01, P03, P05, P07, P10, P12, P14, P16, P18, P21, P23, P25, P27, P30, P32, P34, P36, P38 belonging to the green channel The third determining unit 215 can determine whether the target kernel is a low saturation area based on the second average value AVG2 of the pixel values ​​P11, P13, P15, P17, P31, P33, P35, P37, P51, P53, P55, P57, P71, P73, P75 and P77 belonging to the red channel. For example, when the first average value AVG1 is less than or equal to the second reference value or the first average value AVG1 is less than or equal to the third reference value, the third determining unit 215 can determine that the target kernel is a low saturation area. The second reference value may be a value obtained by subtracting the second threshold value TV2 from the second average value AVG2. The second threshold value TV2 may be a value adaptively adjusted according to the second average value AVG2. The third reference value may be a value obtained by subtracting the third threshold value TV3 from the third average value AVG3. The third threshold value TV3 may be a value adaptively adjusted according to the third average value AVG3.

[0060] Fig. 9 FIG. 1 is a diagram showing a detector (eg, Figure 4 A diagram of the operation of the detector 220) is shown.

[0061] refer to Fig. 9 , when the first reference value obtained by subtracting the first threshold TV1 from the target pixel value P44 is greater than the average value AVG2 of the pixel values ​​P01 to P87 belonging to the green channel and the first reference value is greater than the average value AVG3 of the pixel values ​​P11 to P77 belonging to the red channel, the detector 220 may detect the target pixel value P44 as color noise. The first threshold TV1 may be a value adaptively adjusted according to the target pixel value P44.

[0062] Fig.10 FIG. 1 is a diagram showing a corrector (eg, Figure 4 A diagram of the operation of the corrector 230) is shown.

[0063] refer to Fig.10 , the corrector 230 can correct the color noise by replacing the target pixel value P44 with the correction value. The correction value can be the average value (i.e., AVG (G, R)) of the second average value AVG2 (i.e., GS) of the pixel values ​​P01 to P87 belonging to the green channel and the third average value AVG3 (i.e., RS) of the pixel values ​​P11 to P77 belonging to the red channel. For example, when the target pixel value P44 does not exist in the distribution BS of the pixel values ​​P00 to P88 belonging to the blue channel, the target pixel value P44 can be replaced by the correction value of the average distribution of the distribution GS of the pixel values ​​P01 to P87 belonging to the green channel and the distribution RS of the pixel values ​​P11 to P77 belonging to the red channel.

[0064] Fig.11 is a graph showing denoising performance according to an embodiment of the present disclosure.

[0065] refer to Fig.11 , it can be seen that, unlike the low illumination image A according to some proposed methods, the color noise of the low illumination image B according to the embodiment has been removed. For example, the low illumination image A has degraded image quality due to the cluster color noises N1 and N2 and the impulse color noises N3 and N4 that frequently appear in low saturation and flat areas, but the low illumination image B according to the embodiment can have excellent image quality in which the cluster color noises N1 and N2 and the impulse color noises N3 and N4 are removed.

[0066] According to an embodiment of the present disclosure, color noise of a low-illuminance image can be removed.

[0067] According to an embodiment of the present disclosure, a denoising technology for a low-illuminance image may be supported, thereby obtaining a low-illuminance image with excellent image quality.

[0068] Although the present disclosure has been illustrated and described with reference to specific embodiments, the disclosed embodiments are provided for illustration and are not intended to be limiting. In addition, it should be noted that, as will be appreciated by those skilled in the art in light of the present disclosure, the present disclosure may be implemented in various ways by substitutions, changes, and modifications falling within the scope of the appended claims. The embodiments may be combined to form additional embodiments.

Claims

1. An image sensing device, comprising: an analyzer adapted to analyze a state of each of the plurality of kernels based on the analog gain value and the plurality of pixel values; a detector adapted to detect color noise of a target pixel value among pixel values ​​included in a target kernel among the plurality of kernels according to an analysis result of the analyzer; as well as A corrector is adapted to correct the target pixel value according to the detection result of the detector.

2. The image sensing device according to claim 1, wherein: The detector detects the color noise of the target pixel value when an average value of pixel values ​​belonging to a second color channel among pixel values ​​included in the target kernel is less than or equal to a first reference value, and an average value of pixel values ​​belonging to a third color channel among pixel values ​​included in the target kernel is less than or equal to the first reference value, and The first reference value is a value obtained by subtracting a first threshold value from the target pixel value.

3. The image sensing device according to claim 1, wherein: The analyzer comprises: a first determining component adapted to determine whether a single frame corresponding to the plurality of pixel values ​​has low illumination based on the analog gain value; a second determining means adapted to determine whether the texture of each kernel is a flat area based on the plurality of pixel values; and A third determining component is adapted to determine whether each kernel is a low saturation area based on the plurality of pixel values.

4. The image sensing device according to claim 3, wherein: The third determination component determines whether the target kernel is the low-saturation area based on a first average value of pixel values ​​belonging to a first color channel to which the target pixel value belongs among the pixel values ​​included in the target kernel, a second average value of pixel values ​​belonging to a second color channel among the pixel values ​​included in the target kernel, and a third average value of pixel values ​​belonging to a third color channel among the pixel values ​​included in the target kernel.

5. The image sensing device according to claim 4, wherein: When the first average value is less than or equal to a second reference value or a third reference value, the third determination component determines that the target kernel is the low saturation region, wherein the second reference value is a value obtained by subtracting a second threshold value from the second average value, and The third reference value is a value obtained by subtracting a third threshold value from the third average value.

6. The image sensing device according to claim 3, wherein: The second determination section calculates a standard deviation value based on pixel values ​​belonging to a green channel included in the target kernel, and determines that the texture of the target kernel is the flat area when the standard deviation value is less than or equal to a fourth threshold.

7. The image sensing device according to claim 1, wherein: The corrector corrects the color noise of the target pixel value based on a pixel value belonging to a color channel different from a color channel to which the target pixel value belongs, among pixel values ​​included in the target kernel.

8. An image sensing device, comprising: an image sensor comprising a pixel array in which first pixels, second pixels and third pixels are arranged in a predetermined color pattern, each of the first pixels having a first color filter, each of the second pixels having a second color filter and each of the third pixels having a third color filter; and the image sensor is adapted to generate a plurality of pixel values ​​corresponding to the predetermined color pattern; as well as An image processor is adapted to detect color noise of each of a plurality of kernels based on an analog gain value and the plurality of pixel values, and to detect color noise of a target pixel value included in a target kernel among the plurality of kernels by using all pixel values ​​included in the target kernel.

9. The image sensing device according to claim 8, wherein: The image processor comprises: an analyzer adapted to analyze a state of each kernel based on the analog gain value and the plurality of pixel values; a detector adapted to detect the color noise of the target pixel value according to the analysis result of the analyzer; and A corrector is adapted to correct the target pixel value according to the detection result of the detector.

10. The image sensing device according to claim 9, wherein: The detector detects the color noise of the target pixel value when an average value of pixel values ​​belonging to a second color channel among pixel values ​​included in the target kernel is less than or equal to a first reference value, and an average value of pixel values ​​belonging to a third color channel among pixel values ​​included in the target kernel is less than or equal to the first reference value, and The first reference value is a value obtained by subtracting a first threshold value from the target pixel value.

11. The image sensing device according to claim 9, wherein: The state includes the illumination, saturation, and texture of each kernel.

12. The image sensing device according to claim 9, wherein: The analyzer comprises: a first determining component adapted to determine whether a single frame corresponding to the plurality of pixel values ​​has low illumination based on the analog gain value; a second determining means adapted to determine whether the texture of each kernel is a flat area based on the plurality of pixel values; and A third determining component is adapted to determine whether each kernel is a low saturation area based on the plurality of pixel values.

13. The image sensing device according to claim 12, wherein: The third determination component determines whether the target kernel is the low-saturation area based on a first average value of pixel values ​​belonging to a first color channel to which the target pixel value belongs among the pixel values ​​included in the target kernel, a second average value of pixel values ​​belonging to a second color channel among the pixel values ​​included in the target kernel, and a third average value of pixel values ​​belonging to a third color channel among the pixel values ​​included in the target kernel.

14. The image sensing device according to claim 13, wherein: When the first average value is less than or equal to a second reference value or a third reference value, the third determination component determines that the target kernel is the low saturation region, wherein the second reference value is a value obtained by subtracting a second threshold value from the second average value, and The third reference value is a value obtained by subtracting a third threshold value from the third average value.

15. The image sensing device according to claim 12, wherein: The second determination section calculates a standard deviation value based on pixel values ​​belonging to a green channel included in the target kernel, and determines that the texture of the target kernel is the flat area when the standard deviation value is less than or equal to a fourth threshold.

16. The image sensing device according to claim 9, wherein: The corrector corrects the color noise of the target pixel value based on pixel values ​​belonging to a color channel different from a color channel to which the target pixel value belongs among pixel values ​​included in the target kernel.

17. A method for operating an image sensing device, comprising: determining saturation and texture for each of the plurality of kernels; When a target kernel among the plurality of kernels is a low saturation region rather than a high saturation region and is a flat region, detecting color noise of a target pixel value among pixel values ​​included in the target kernel; and The color noise of the target pixel value is corrected.

18. The operating method according to claim 17, further comprising determining whether a single frame has low illumination based on the analog gain value, in, When the single frame has low illumination, a determination of saturation and texture for each kernel is performed.

19. An image sensing device, comprising: an image sensor comprising a pixel array adapted to generate a plurality of pixel values ​​corresponding to a set frame; as well as Image processor suitable for: determining whether the frame has low illumination; When it is determined that the frame has low illumination, selecting a pixel value in the frame corresponding to a target kernel; Determining whether the target kernel is a flat region and a low saturation region; When it is determined that the target kernel is the flat area and the low-saturation area, detecting whether the target pixel value corresponds to color noise based on other pixel values ​​of the target kernel having other color channels different from the color channel of the target pixel value of the target kernel; as well as When it is detected that the target kernel corresponds to the color noise, the target pixel value is corrected using the other pixel values.

Images