Image processing method and device, image processing equipment and storage medium

Abstract

The present disclosure relates to an image processing method and device, an image processing apparatus and a storage medium, wherein the method comprises: adding a preset noise signal to a to-be-processed image signal containing noise to obtain a mixed signal; determining a signal-to-noise ratio of the mixed signal, and judging whether the signal-to-noise ratio meets a preset screening condition; if the preset screening condition is met, determining an actual noise signal in the mixed signal, and performing filtering processing on the actual noise signal to obtain a target image signal after noise removal. In this way, by adding noise to the to-be-processed image signal, a mixed signal with a signal-to-noise ratio meeting the preset screening condition can be obtained, and the signal-to-noise ratio meeting the preset screening condition is more conducive to noise removal.

Description

Technical Field

[0001] This disclosure relates to the field of image technology, and in particular to an image processing method and apparatus, an image processing device, and a storage medium. Background Technology

[0002] In low-light or dimly lit environments, image processing is crucial and directly impacts the user experience. Because the grayscale values ​​received by the camera's sensor are very similar in low light, it's difficult to distinguish between the target object and its surroundings, resulting in noisy images and poor image quality. Therefore, image denoising is necessary. However, current denoising techniques, while filtering out noise, also filter out some useful signals, leading to less than ideal denoising results. Summary of the Invention

[0003] This disclosure provides an image processing method and apparatus, an image processing device, and a storage medium.

[0004] According to a first aspect of the present disclosure, an image processing method is provided, comprising:

[0005] A preset noise signal is added to the image signal to be processed, which contains noise, to obtain a mixed signal;

[0006] Determine the signal-to-noise ratio of the mixed signal and determine whether the signal-to-noise ratio meets the preset screening conditions;

[0007] When the signal-to-noise ratio meets the preset screening conditions, the actual noise signal in the mixed signal is determined, and the actual noise signal is filtered to obtain the denoised target image signal.

[0008] Optionally, determining the actual noise signal in the mixed signal and filtering the actual noise signal to obtain the denoised target image signal includes:

[0009] The mixed signal is processed in the frequency domain to obtain the amplitude distribution characteristics of the mixed signal in the frequency domain;

[0010] The frequency range of the actual noise signal in the mixed signal is determined based on the amplitude distribution characteristics.

[0011] Based on the frequency range, the actual noise signal is filtered to obtain the denoised target image signal.

[0012] Optionally, determining the frequency range of the actual noise signal in the mixed signal based on the amplitude distribution characteristics includes:

[0013] The maximum and second maximum amplitude values ​​are determined based on the amplitude distribution characteristics of the mixed signal in the frequency domain.

[0014] According to the preset frequency length, the frequency range including the second amplitude value is determined as the frequency range of the actual noise signal in the mixed signal.

[0015] Optionally, the method further includes:

[0016] Obtain a model of the target nonlinear modulation system in a stable state;

[0017] The step of adding a preset noise signal to the noisy image signal to obtain a mixed signal includes:

[0018] The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model to obtain the mixed signal.

[0019] Optionally, obtaining the target nonlinear modulation system model in a stable state includes:

[0020] Establish an initial nonlinear modulation system model;

[0021] Based on a preset first adjustment step size, the initial structural parameters of the initial nonlinear modulation system model are adjusted to obtain a target nonlinear modulation system model in a stable state.

[0022] Optionally, the step of inputting the image signal to be processed and the preset noise signal into the target nonlinear modulation system model to obtain the mixed signal includes:

[0023] The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model;

[0024] According to the preset second adjustment step size, the intensity of the preset noise signal in the target nonlinear modulation system model is adjusted sequentially to obtain multiple different mixed signals.

[0025] Optionally, the preset filtering conditions include one of the following:

[0026] The signal-to-noise ratio of the mixed signal is greater than a preset threshold;

[0027] The signal-to-noise ratio of the mixed signal reaches its maximum value; wherein, the signal-to-noise ratio reaches its maximum value when the relationship between the signal-to-noise ratio of the mixed signal and the intensity of the preset noise signal changes from positive to negative.

[0028] Optionally, the step of adding a preset noise signal to the noisy image signal to obtain a mixed signal includes:

[0029] When the image signal to be processed is an image signal acquired when the light intensity is lower than the intensity threshold, a preset noise signal is added to the image signal to be processed to obtain a mixed signal.

[0030] According to a second aspect of the present disclosure, an image processing apparatus is provided, comprising:

[0031] The noise addition module is used to add a preset noise signal to the image signal to be processed, which contains noise, to obtain a mixed signal;

[0032] The signal-to-noise ratio (SNR) determination module is used to determine the SNR of the mixed signal and to determine whether the SNR meets preset screening conditions.

[0033] The denoising module is used to determine the actual noise signal in the mixed signal if the preset screening conditions are met, and to filter the actual noise signal to obtain the denoised target image signal.

[0034] Optionally, the noise reduction module includes:

[0035] A frequency determination module is used to perform frequency domain processing on the mixed signal to obtain the amplitude distribution characteristics of the mixed signal in the frequency domain.

[0036] A range determination module is used to determine the frequency range of the actual noise signal in the mixed signal based on the amplitude distribution characteristics.

[0037] The processing module is used to filter the actual noise signal according to the frequency range to obtain the denoised target image signal.

[0038] Optionally, the range determination module is also used for:

[0039] The maximum and second maximum amplitude values ​​are determined based on the amplitude distribution characteristics of the mixed signal in the frequency domain.

[0040] According to the preset frequency length, the frequency range including the second amplitude value is determined as the frequency range of the actual noise signal in the mixed signal.

[0041] Optionally, the device further includes:

[0042] The target model acquisition module is used to acquire the target nonlinear modulation system model in a stable state.

[0043] The noise-adding module includes:

[0044] The noise-adding submodule is used to input the image signal to be processed and the preset noise signal into the target nonlinear modulation system model to obtain the mixed signal.

[0045] Optionally, the target model acquisition module includes:

[0046] A module is established to build the initial nonlinear modulation system model;

[0047] The adjustment module is used to adjust the initial structural parameters of the initial nonlinear modulation system model based on a preset first adjustment step size, so as to obtain a target nonlinear modulation system model in a stable state.

[0048] Optionally, the noise-adding submodule is further configured to:

[0049] The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model;

[0050] According to the preset second adjustment step size, the intensity of the preset noise signal in the target nonlinear modulation system model is adjusted sequentially to obtain multiple different mixed signals.

[0051] Optionally, the preset filtering conditions include one of the following:

[0052] The signal-to-noise ratio of the mixed signal is greater than a preset threshold;

[0053] The signal-to-noise ratio of the mixed signal reaches its maximum value; wherein, the signal-to-noise ratio reaches its maximum value when the relationship between the signal-to-noise ratio of the mixed signal and the intensity of the preset noise signal changes from positive to negative.

[0054] Optionally, the noise-adding module is further configured to:

[0055] When the image signal to be processed is an image signal acquired when the light intensity is lower than the intensity threshold, a preset noise signal is added to the image signal to be processed to obtain a mixed signal.

[0056] According to a third aspect of the present disclosure, an image processing apparatus is provided, comprising:

[0057] processor;

[0058] Memory used to store processor-executable instructions;

[0059] The processor is configured to, when executing executable instructions stored in the memory, implement the method described in any of the first aspects above.

[0060] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein computer-executable instructions are stored therein, which, when executed by a processor, implement the steps of the method provided in any of the first aspects described above.

[0061] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0062] The image processing method provided in this disclosure adds a preset noise signal to the image signal to be processed, resulting in a mixed signal containing both the preset noise signal and the image signal to be processed. Since the added preset noise signal may partially cancel out the original noise signal in the image signal to be processed, the signal-to-noise ratio (SNR) of the mixed signal can be improved. Thus, a mixed signal whose SNR meets a preset filtering condition can be selected to obtain the target image signal for denoising. In this way, during image processing, noise cancellation reduces the noise signal contained in the image signal itself. Therefore, after the SNR of the mixed signal reaches the preset filtering condition, denoising is more convenient and the denoising effect is better.

[0063] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0065] Figure 1 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 1 .

[0066] Figure 2 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 2 .

[0067] Figure 3 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 3 .

[0068] Figure 4 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 4 .

[0069] Figure 5 This is a schematic diagram of the structure of an image processing apparatus according to an exemplary embodiment.

[0070] Figure 6 This is a block diagram illustrating an image processing apparatus according to an exemplary embodiment. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0072] This disclosure provides an image processing method. Figure 1 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 1 ,like Figure 1 As shown, the image processing method includes the following steps:

[0073] Step 101: Add a preset noise signal to the image signal to be processed that contains noise to obtain a mixed signal;

[0074] Step 102: Determine the signal-to-noise ratio of the mixed signal and determine whether the signal-to-noise ratio meets the preset screening conditions;

[0075] Step 103: If the preset screening conditions are met, the actual noise signal in the mixed signal is determined, and the actual noise signal is filtered to obtain the denoised target image signal.

[0076] It should be noted that this image processing method can be applied to any electronic device, such as a smartphone, tablet, or wearable electronic device.

[0077] In this embodiment of the disclosure, the image signal to be processed is a noisy signal, and it is necessary to remove or reduce the noise in the image signal to be processed in order to improve the quality of the image signal to be processed.

[0078] When the useful signal and noise signal-to-noise ratio (SNR) of the image signal to be processed are relatively similar, many details will be lost if current filtering methods (such as mean filtering or bandpass filtering) are used to denoise such images. Therefore, effective denoising processing is needed for such signals where the useful signal and noise SNR are relatively similar.

[0079] Here, the mixed signal is the signal obtained by mixing the image signal to be processed and the preset noise signal. The mixed signal contains the preset noise signal and the noise signal contained in the image signal to be processed.

[0080] The noise signals contained in the preset noise signal and the image signal to be processed can be any noise signals, such as white noise, salt-and-pepper noise, or Gaussian noise. The noise signals contained in the preset noise signal and the image signal to be processed in the mixed signal can be different types of noise or the same type of noise. This disclosure does not limit the types of the two noise signals.

[0081] Since image acquisition in low light or dim light conditions often results in similar grayscale values ​​received by the image sensor, this embodiment focuses on denoising image signals where the grayscale values ​​of useful and noise signals are similar to improve image quality. Thus, in some embodiments, step 101, which involves adding a preset noise signal to the noisy image signal to obtain a mixed signal, may include: when the image signal to be processed is an image signal acquired when the light intensity is below an intensity threshold, adding a preset noise signal to the image signal to obtain a mixed signal.

[0082] The intensity threshold can be determined based on the imaging condition of the image. That is, when it is determined that the imaging quality is insufficient, the light intensity of the image with insufficient imaging quality at the time of acquisition is obtained, and the intensity threshold is determined by combining the light intensities corresponding to multiple images with insufficient imaging quality.

[0083] Here, since the gray values ​​of the useful signal and the noise signal in the image signal to be processed are relatively close, in order to remove the noise signal while minimizing the impact on the useful signal, such as... Figure 2 As shown, Figure 2 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 2 In this embodiment, a preset noise signal is added to the image signal to be processed. By adjustment, the preset noise signal and the noise signal contained in the image signal to be processed can be partially canceled out, thereby improving the signal-to-noise ratio of the mixed signal.

[0084] It should be noted that regarding the cancellation of noise signals contained in the preset noise signal and the image signal to be processed, since noise signals are irregular, if two noise signals have opposite phases at the same frequency (i.e., same frequency, opposite phase), they will cancel each other out at that frequency. Specifically, when corresponding to the same frequency, if two noise signals have opposite phases and equal amplitudes, they will cancel each other out at that frequency. If, when corresponding to the same frequency, two noise signals have opposite phases but unequal amplitudes, they can partially cancel each other out.

[0085] After mixing two noise signals, the goal is to partially cancel out the noise signals contained in the preset noise signal and the image signal to be processed. Therefore, to achieve better cancellation results, the energy of the added preset noise signal needs to be controlled. The more energy the added preset noise signal has, the stronger the preset noise signal becomes.

[0086] In this embodiment of the disclosure, after a preset noise signal is added to the image signal to be processed to obtain a mixed signal, the cancellation effect of the two noise signals is determined by determining the signal-to-noise ratio of the mixed signal.

[0087] Here, when the signal-to-noise ratio (SNR) of the mixed signal meets the preset screening criteria, it is considered that the mixed signal has met the denoising requirement without excessive loss of useful signal. At this point, the mixed signal corresponding to the SNR that meets the preset screening criteria is subjected to denoising processing to obtain the denoised target image signal.

[0088] In this embodiment of the disclosure, the preset filtering conditions may include:

[0089] The signal-to-noise ratio of the mixed signal is greater than the threshold.

[0090] In order to achieve cancellation and determine the cancellation effect, this disclosure involves adding preset noise signals of different intensities to the image signal to be processed to obtain multiple different mixed signals, determining the signal-to-noise ratio (SNR) of multiple mixed signals, identifying the SNR greater than a threshold from the multiple SNRs, further identifying the actual noise signal in the mixed signal with an SNR greater than the threshold, and performing denoising processing on the actual noise signal to obtain the denoised target image signal.

[0091] Here, since the noise signal is chaotic and irregular, it is impossible to determine how much noise can achieve a better cancellation effect when adding noise. In this embodiment, different mixed signals are obtained by adding preset noise signals of different intensities to the image signal to be processed, the signal-to-noise ratio of each mixed signal is determined, and the mixed signal with a signal-to-noise ratio greater than a threshold is selected as the mixed signal to be denoised. In this way, since the energy difference between the useful signal and the noise signal is large in the mixed signal with a signal-to-noise ratio greater than the threshold, denoising is more convenient and the useful signal is lost as little as possible.

[0092] It should also be noted that during the test, there may be a situation where the signal-to-noise ratio of multiple mixed signals is greater than the threshold. In this disclosure, the first mixed signal with a signal-to-noise ratio greater than the threshold in the test can be selected as the mixed signal to be denoised, or, arbitrarily selected from multiple mixed signals with a signal-to-noise ratio greater than the threshold can be selected as the mixed signal to be denoised.

[0093] In the test, equal amounts of preset noise signals can be gradually added to the image signal to be processed. For example, if 50 dB of preset noise is added to the image signal A first, the mixed signal is A + 50 dB. Assuming the signal-to-noise ratio (SNR) of this mixed signal is B, the intensity of the corresponding preset noise signal is 50 dB. The second time, another 50 dB of noise is added on top of the first addition, resulting in a mixed signal of A + 100 dB. Assuming the SNR of this mixed signal is C, the intensity of the corresponding preset noise signal is 100 dB, and so on. In this process of gradually adding equal amounts of preset noise signals to the image signal to be processed, a first mixed signal with an SNR greater than a threshold may appear. This first mixed signal with an SNR greater than the threshold can be used as the mixed signal to be denoised, or any one of multiple mixed signals with an SNR greater than the threshold can be selected as the mixed signal to be denoised. This disclosure does not impose any limitations on this.

[0094] In other embodiments, the preset filtering conditions may further include:

[0095] The signal-to-noise ratio reaches its maximum value; wherein, when the relationship between the signal-to-noise ratio of the mixed signal and the intensity of the preset noise signal changes from positive to negative, the signal-to-noise ratio reaches its maximum value when the relationship changes from positive to negative.

[0096] This involves denoising the mixed signal based on the signal-to-noise ratio (SNR) to obtain the denoised target image signal. Since the energy difference between the useful and noise signals is the largest in the mixed signal corresponding to the maximum SNR, there are more denoising algorithms to choose from, denoising is more convenient, and the loss of useful signals can be reduced more significantly.

[0097] Here, as the intensity of the preset noise signal increases, the signal-to-noise ratio (SNR) of the corresponding mixed signal will first increase and then decrease. That is, the relationship between the SNR of the mixed signal and the intensity of the preset noise signal will change from a positive correlation to a negative correlation. This is reflected in the relationship curve as an initial rise followed by a fall, resulting in a peak point on the curve. This peak point represents the SNR (or maximum SNR) when the relationship changes from a positive correlation to a negative correlation.

[0098] In some embodiments, Figure 3 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 3 ,like Figure 3 As shown, in step 103 above, determining the actual noise signal in the mixed signal and filtering the actual noise signal to obtain the denoised target image signal includes:

[0099] Step 1031: Perform frequency domain processing on the mixed signal to obtain the amplitude distribution characteristics of the mixed signal in the frequency domain;

[0100] Step 1032: Determine the frequency range of the actual noise signal in the mixed signal based on the amplitude distribution characteristics;

[0101] Step 1033: Filter the actual noise signal according to the frequency range to obtain the denoised image signal.

[0102] Here, after determining the mixed signal whose signal-to-noise ratio meets the preset screening conditions, since the mixed signal whose signal-to-noise ratio meets the preset screening conditions is a time-domain signal, in order to more easily distinguish the useful signal and the noise signal and to facilitate the denoising process, the embodiments of this disclosure perform frequency domain processing on the mixed signal whose signal-to-noise ratio meets the preset screening conditions to obtain a frequency domain signal. Based on the frequency domain signal, the frequency range of the noise signal in the mixed signal whose signal-to-noise ratio meets the preset screening conditions is determined. Then, based on the frequency range, the actual noise signal is filtered to obtain the denoised image signal.

[0103] Here, the frequency domain signal obtained after frequency domain processing is a signal that reflects the relationship between the frequency and amplitude of the mixed signal, showing the amplitude distribution characteristics of the mixed signal in the frequency domain. For example, it can reflect which frequency ranges the mixed signal has a large amplitude or which frequency range the maximum amplitude corresponds to.

[0104] The frequency domain processing can be Fourier transform. After performing Fourier transform on the mixed signal whose signal-to-noise ratio meets the preset screening conditions to obtain the frequency domain signal, the inverse Fourier transform can restore it to the time domain signal.

[0105] In some embodiments, determining the frequency range of the actual noise signal in the mixed signal based on the amplitude distribution characteristics includes:

[0106] The maximum and second maximum amplitude values ​​are determined based on the amplitude distribution characteristics of the mixed signal in the frequency domain.

[0107] According to the preset frequency length, the frequency range including the second amplitude value is determined as the frequency range of the actual noise signal in the mixed signal.

[0108] Here, the preset frequency length can be determined based on the number of amplitudes centered on the second amplitude value whose difference from the second amplitude value is less than a threshold.

[0109] Since the mixed signal whose signal-to-noise ratio meets the preset screening conditions is already a mixed signal with a large difference between useful signal and noise signal, the corresponding frequency domain signal is as follows: the frequency range formed by the frequency corresponding to the maximum amplitude value and its surrounding adjacent frequencies is the frequency range of useful information, while the frequency range formed by the frequency corresponding to the second amplitude value and its surrounding adjacent frequencies is the frequency range of noise information; therefore, the frequency range of the actual noise signal in the mixed signal is the frequency range including the second amplitude value.

[0110] Thus, the frequency range of the actual noise signal in the mixed signal can be directly determined based on the amplitude distribution characteristics of the mixed signal in the frequency domain. Furthermore, after determining the frequency range of the actual noise signal in the mixed signal, a corresponding filter can be selected to filter this frequency range, obtaining the denoised target image signal and achieving noise removal. Here, the filter can be a bandpass filter or a bandstop filter, etc.

[0111] Since the signal-to-noise ratio of the mixed signal to be denoised meets the preset screening conditions, the difference between the useful signal and the noise signal is relatively obvious. At this time, noise filtering based on the amplitude distribution characteristics in the frequency domain can achieve a very good denoising effect and make denoising more convenient.

[0112] In some embodiments, the method further includes:

[0113] Obtain a model of the target nonlinear modulation system in a stable state;

[0114] The step of adding a preset noise signal to the noisy image signal to obtain a mixed signal includes:

[0115] The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model to obtain the mixed signal.

[0116] In this embodiment of the present disclosure, the image signal to be processed and the preset noise signal are processed by a target nonlinear modulation system model to obtain a mixed signal.

[0117] The target nonlinear modulation system model is a model in a stable state. Processing the signal using a model in a stable state can minimize the possibility of insufficient signal-to-noise ratio improvement due to excessive or insufficient noise.

[0118] Here, the target nonlinear modulation system model is described. In this embodiment, the initial nonlinear modulation system model is adjusted to obtain the target nonlinear modulation system model. Specifically:

[0119] In some embodiments, obtaining the target nonlinear modulation system model in a stable state includes:

[0120] Establish an initial nonlinear modulation system model;

[0121] Based on a preset first adjustment step size, the initial structural parameters of the initial nonlinear modulation system model are adjusted to obtain a target nonlinear modulation system model in a stable state.

[0122] In this embodiment of the disclosure, the initial nonlinear modulation system model can be a stochastic resonance system model or a chaotic system model. Here, the stochastic resonance system model is used as an example for illustration.

[0123] The stochastic resonance system model can be represented by the nonlinear Langevin equation, as shown below:

[0124]

[0125] Where x is the output signal of the initial stochastic resonance system model, t is the time variable, s(t) is the input signal of the initial stochastic resonance system model (i.e., the image signal to be processed), n(t) is the added preset noise signal, a is the coefficient of x, and b is the coefficient of x. 3 The coefficients a and b are also called the structural parameters of the initial stochastic resonance system model.

[0126] Here, the initial stochastic resonance system model can be brought to a stable state by adjusting the initial structural parameters of the initial nonlinear modulation system model. Specifically, the initial structural parameters and the corresponding preset first adjustment step size can be obtained, and the adjusted stochastic resonance system model (i.e., the target nonlinear modulation system model) can be obtained based on the initial structural parameters and the corresponding preset first adjustment step size.

[0127] The initial structural parameters and the preset first adjustment step size can be selected based on the needs to match the characteristics of the image signal to be processed, such as frequency or amplitude. Selecting appropriate initial structural parameters and preset first adjustment step size is beneficial to obtaining the target nonlinear modulation system model more quickly.

[0128] As a concrete example, the initial structural parameters can be set to a = b = 1, with a preset first adjustment step size of 0.2. By gradually changing the values ​​of a and b, a target stochastic resonance system model in a stable state can be obtained. For example, with the initial structural parameters a = b = 1, adjusting the structural parameters to increase a or decrease b, such as a = 1.4 and b = 0.6, will yield a stochastic resonance system model; further adjustment, such as a = 2 and b = 0, will yield another stochastic resonance system model, and so on.

[0129] After obtaining multiple stochastic resonance system models, the image signal to be processed and the preset noise signal are processed based on these models, resulting in corresponding waveforms. Different models produce different output waveforms, allowing the determination of the target stochastic resonance system model based on the state of the output waveform. For example, some stochastic resonance system models output waveforms containing numerous glitches, while others output waveforms with insignificant vibration amplitudes; both are considered to be in a state of instability. When the clarity of the output waveform reaches a clarity threshold and exhibits significant vibration amplitude, the corresponding stochastic resonance system model is the target stochastic resonance system model.

[0130] After obtaining the target stochastic resonance system model, a preset noise signal and an image signal to be processed are input into the target stochastic resonance system model. For each input of the preset noise signal and the image signal to be processed, an output signal is input into the target stochastic resonance system model. This output signal is the mixed signal, and thus, multiple mixed signals can be obtained.

[0131] Here, the processing of the preset noise signal and the image signal to be processed in the target stochastic resonance system model is explained in detail:

[0132] In some embodiments, inputting the image signal to be processed and the preset noise signal into the target nonlinear modulation system model to obtain the mixed signal includes:

[0133] The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model;

[0134] According to the preset second adjustment step size, the intensity of the preset noise signal in the target nonlinear modulation system model is adjusted sequentially to obtain multiple different mixed signals.

[0135] In this embodiment of the disclosure, after adding the image signal to be processed to the target stochastic resonance system model, the preset noise signal can be added gradually in equal amounts. That is, a preset second adjustment step size corresponding to the preset noise signal is set, and the preset noise signal is added sequentially according to the preset second adjustment step size. For example, assuming that the preset second adjustment step size corresponding to the preset noise signal is 50, then the image signal to be processed A and 50dB of noise signal are added for the first time, and the corresponding preset noise signal is 50dB at this time; then, another 50dB of noise signal is added for the second time, and the corresponding preset noise signal is 100dB at this time; and so on.

[0136] Therefore, for each input image signal to be processed and preset noise signal, the target stochastic resonance system model outputs a mixed signal. Since the noise signals contained in the preset noise signal and the image signal to be processed are canceled out of phase and at the same frequency in the target stochastic resonance system model, the waveform of the output mixed signal is greatly improved compared to the image signal to be processed input into the target stochastic resonance system model, and the signal-to-noise ratio is improved.

[0137] Thus, after processing by the target stochastic resonance system model, a mixed signal with improved signal-to-noise ratio is obtained. From this mixed signal with improved signal-to-noise ratio, if the mixed signal with the corresponding signal-to-noise ratio that meets the preset screening conditions is further selected for denoising, the denoising effect can be further improved.

[0138] In some embodiments, when processing the target nonlinear modulation system model, the amplitude of the added preset noise signal can be controlled to further achieve a good cancellation effect. When corresponding to the same frequency, if the two noise signals are out of phase, the smaller the difference in amplitude, the more the two noise signals will cancel each other out at that frequency.

[0139] In this embodiment of the disclosure, controlling the amplitude of the added preset noise signal can be achieved by selecting a noise signal with the desired amplitude each time the preset noise signal is added. The noise signal with the desired amplitude can be a noise signal at different frequency ranges within the same type of noise signal, or it can be a noise signal of a different type. For example, assuming that a noise signal with amplitude A is currently to be added, a portion of the noise signal whose amplitude satisfies amplitude A can be selected from noise signals of the same type as the previously added noise signal and used as the noise signal to be added this time. Alternatively, another type of noise signal that satisfies amplitude A can be selected as the noise signal to be added this time.

[0140] It should also be noted that adding a preset noise signal to the image signal to obtain a mixed signal can also be achieved through a neural network model. Specifically, in historical experiments, the image signal to be processed, the preset noise signal, and the mixed signal are used as training data to train the neural network model, resulting in a target neural network model. This target neural network model then processes the image signal to be processed and the preset noise signal, outputting the mixed signal. Furthermore, adjusting the intensity of the preset noise signal allows for the output of different mixed signals.

[0141] In some embodiments, the method further includes:

[0142] When the relationship between the signal-to-noise ratio of the mixed signal and the intensity of the preset noise signal changes from positive to negative, the target signal-to-noise ratio when the positive correlation changes to negative correlation, and the mixed signal corresponding to the target signal-to-noise ratio, are determined.

[0143] Based on the mixed signal corresponding to the target signal-to-noise ratio, the actual noise signal in the mixed signal is determined.

[0144] In this embodiment of the disclosure, the preset screening condition for the signal-to-noise ratio (SNR) of the mixed signal is that the SNR reaches its maximum value. When the SNR reaches its maximum value, the mixed signal corresponding to the maximum SNR can be determined, and denoising processing is performed based on the mixed signal corresponding to the maximum SNR to obtain a denoised image signal. The target SNR when the relationship between the SNR of the mixed signal and the intensity of the preset noise signal changes from positive to negative is the SNR when the SNR reaches its maximum value.

[0145] Here, as the intensity of the preset noise signal increases, the signal-to-noise ratio (SNR) of the corresponding mixed signal will first increase and then decrease. That is, the relationship between the SNR of the mixed signal and the intensity of the preset noise signal will change from a positive correlation to a negative correlation. This is reflected in the relationship curve as an initial rise followed by a fall, resulting in a peak point on the curve. This peak point is the target SNR (or maximum SNR).

[0146] After finding the mixed signal corresponding to the maximum signal-to-noise ratio, the mixed signal is denoised to obtain the denoised image signal.

[0147] This disclosure also provides the following embodiments:

[0148] Figure 4 This is a flowchart of an image processing method according to an exemplary embodiment. Figure 4 ,like Figure 4 As shown, the image processing method includes the following steps:

[0149] Step 401: Acquire the original image and determine if the image is clear.

[0150] Here, image sharpness can be determined by the clarity of details and boundaries within the image. Specifically, a sharpness evaluation function can be used to assess whether the image meets a sharpness threshold. This function can be a gray-level variance function, a Brenner gradient function, or a Laplacian function, among others.

[0151] Step 402: When the image is unclear, obtain the image signal to be processed corresponding to the image.

[0152] Here, the image signal to be processed includes: useful signal and noise signal.

[0153] After obtaining the original image, it is first determined whether the image is clear. If the obtained image is not clear, that is, the gray values ​​of the useful signal and the noise signal in the image are relatively close, then the corresponding image signal to be processed is obtained based on the image.

[0154] Step 403: Add a preset noise signal to the image signal to be processed.

[0155] Here, one or more preset noise signals are mixed with the original image signal to be processed.

[0156] The preset noise signal can be: Gaussian noise, white noise, or dark light noise obtained in the laboratory to simulate a real environment.

[0157] Step 404: Based on the target nonlinear modulation system model, the image signal to be processed and the preset noise signal are processed to obtain a mixed signal.

[0158] Step 405: Determine the signal-to-noise ratio of the mixed signal, and identify the mixed signal whose signal-to-noise ratio meets the preset screening conditions from multiple mixed signals.

[0159] Step 406: Perform frequency domain processing on the mixed signal whose signal-to-noise ratio meets the preset screening conditions to obtain a frequency domain signal.

[0160] Here, the mixed signal obtained after processing by the target nonlinear modulation system model is still in the time domain. Frequency domain processing (such as Fourier transform) is performed on the mixed signal to obtain the frequency domain signal.

[0161] Step 407: Based on the frequency domain signal, determine the frequency range of the noise signal in the mixed signal whose signal-to-noise ratio meets the preset screening conditions.

[0162] Here, the amplitude distribution characteristics of the mixed signal in the frequency domain can be obtained first based on the frequency domain signal; then, based on the amplitude distribution characteristics, the frequency range of the noise signal in the mixed signal whose signal-to-noise ratio meets the preset screening conditions can be determined.

[0163] In this embodiment of the disclosure, one or more pairs of signals with amplitude at the second position in the frequency domain signal are determined to be noise signals under low light.

[0164] Step 408: Based on the frequency range, filter the actual noise signal to obtain the denoised target image signal.

[0165] Thus, by adding an appropriate preset noise signal, the preset noise signal may partially cancel out the original noise signal in the image signal to be processed, thereby improving the signal-to-noise ratio (SNR) of the mixed signal. This allows for the selection of the mixed signal whose SNR meets the preset filtering conditions to obtain the denoised image signal. In this way, once the SNR of the mixed signal reaches the preset filtering conditions, denoising becomes more convenient and the denoising effect is better.

[0166] This disclosure also provides an image processing apparatus. Figure 5 This is a schematic diagram illustrating the structure of an image processing apparatus according to an exemplary embodiment, such as... Figure 5 As shown, the image processing device 500 includes:

[0167] The noise-adding module 501 is used to add a preset noise signal to the image signal to be processed containing noise to obtain a mixed signal;

[0168] The signal-to-noise ratio determination module 502 is used to determine the signal-to-noise ratio of the mixed signal and to determine whether the signal-to-noise ratio meets the preset screening conditions.

[0169] The denoising module 503 is used to determine the actual noise signal in the mixed signal if the preset screening conditions are met, and to filter the actual noise signal to obtain the denoised target image signal.

[0170] In some embodiments, the noise reduction module includes:

[0171] A frequency determination module is used to perform frequency domain processing on the mixed signal to obtain the amplitude distribution characteristics of the mixed signal in the frequency domain.

[0172] A range determination module is used to determine the frequency range of the actual noise signal in the mixed signal based on the amplitude distribution characteristics.

[0173] The processing module is used to filter the actual noise signal according to the frequency range to obtain the denoised target image signal.

[0174] In some embodiments, the range determination module is further configured to:

[0175] The maximum and second maximum amplitude values ​​are determined based on the amplitude distribution characteristics of the mixed signal in the frequency domain.

[0176] According to the preset frequency length, the frequency range including the second amplitude value is determined as the frequency range of the actual noise signal in the mixed signal.

[0177] In some embodiments, the apparatus further includes:

[0178] The target model acquisition module is used to acquire the target nonlinear modulation system model in a stable state.

[0179] The noise-adding module includes:

[0180] The noise-adding submodule is used to input the image signal to be processed and the preset noise signal into the target nonlinear modulation system model to obtain the mixed signal.

[0181] In some embodiments, the target model acquisition module includes:

[0182] A module is established to build the initial nonlinear modulation system model;

[0183] The adjustment module is used to adjust the initial structural parameters of the initial nonlinear modulation system model based on a preset first adjustment step size, so as to obtain a target nonlinear modulation system model in a stable state.

[0184] In some embodiments, the noise-adding submodule is further configured to:

[0185] The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model;

[0186] According to the preset second adjustment step size, the intensity of the preset noise signal in the target nonlinear modulation system model is adjusted sequentially to obtain multiple different mixed signals.

[0187] In some embodiments, the preset filtering conditions include one of the following:

[0188] The signal-to-noise ratio of the mixed signal is greater than a preset threshold;

[0189] The signal-to-noise ratio of the mixed signal reaches its maximum value; wherein, the signal-to-noise ratio reaches its maximum value when the relationship between the signal-to-noise ratio of the mixed signal and the intensity of the preset noise signal changes from positive to negative.

[0190] In some embodiments, the noise-adding module is further configured to:

[0191] When the image signal to be processed is an image signal acquired when the light intensity is lower than the intensity threshold, a preset noise signal is added to the image signal to be processed to obtain a mixed signal.

[0192] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0193] Figure 6 This is a block diagram illustrating an image processing device or image processing apparatus 1800 according to an exemplary embodiment. For example, apparatus 1800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0194] Reference Figure 6The device 1800 may include one or more of the following components: a processing component 1802, a memory 1804, a power component 1806, a multimedia component 1808, an audio component 1810, an input / output (I / O) interface 1812, a sensor component 1814, and a communication component 1816.

[0195] Processing component 1802 typically controls the overall operation of device 1800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 1802 may include one or more processors 1820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 1802 may also include one or more modules to facilitate interaction between processing component 1802 and other components. For example, processing component 1802 may include a multimedia module to facilitate interaction between multimedia component 1808 and processing component 1802.

[0196] Memory 1804 is configured to store various types of data to support the operation of device 1800. Examples of this data include instructions for any application or method operating on device 1800, contact data, phonebook data, messages, images, videos, etc. Memory 1804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0197] The power supply component 1806 provides power to the various components of the device 1800. The power supply component 1806 may include: a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 1800.

[0198] Multimedia component 1808 includes a screen that provides an output interface between the device 1800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 1808 includes a front-facing camera and / or a rear-facing camera. When the device 1800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and / or rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0199] Audio component 1810 is configured to output and / or input audio signals. For example, audio component 1810 includes a microphone (MIC) configured to receive external audio signals when device 1800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1804 or transmitted via communication component 1816. In some embodiments, audio component 1810 also includes a speaker for outputting audio signals.

[0200] I / O interface 1812 provides an interface between processing component 1802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0201] Sensor assembly 1814 includes one or more sensors for providing status assessments of various aspects of device 1800. For example, sensor assembly 1814 may detect the on / off state of device 1800, the relative positioning of components such as the display and keypad of device 1800, changes in the position of device 1800 or a component of device 1800, the presence or absence of user contact with device 1800, the orientation or acceleration / deceleration of device 1800, and temperature changes of device 1800. Sensor assembly 1814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 1814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0202] Communication component 1816 is configured to facilitate wired or wireless communication between device 1800 and other devices. Device 1800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 1816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, or other technologies.

[0203] In an exemplary embodiment, the apparatus 1800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0204] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1804 including instructions, which can be executed by a processor 1820 of the device 1800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0205] A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor, enable the execution of the above-described method.

[0206] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0207] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An image processing method, characterized in that, include: A preset noise signal is added to the image signal to be processed, which contains noise, to obtain a mixed signal; the mixed signal includes multiple signals, and the multiple mixed signals are obtained by adding different preset noise signals to the image to be processed; Determine the signal-to-noise ratio of the mixed signal and determine whether the signal-to-noise ratio meets the preset screening conditions; If the preset filtering conditions are met, the actual noise signal in the mixed signal whose signal-to-noise ratio meets the preset filtering conditions is determined, and the actual noise signal is filtered to obtain the denoised target image signal.

2. The method according to claim 1, characterized in that, The process of determining the actual noise signal in the mixed signal whose signal-to-noise ratio satisfies the preset screening conditions, and filtering the actual noise signal to obtain the denoised target image signal includes: For the mixed signal whose signal-to-noise ratio meets the preset screening conditions, the mixed signal is processed in the frequency domain to obtain the amplitude distribution characteristics of the mixed signal in the frequency domain. The frequency range of the actual noise signal in the mixed signal is determined based on the amplitude distribution characteristics. Based on the frequency range, the actual noise signal is filtered to obtain the denoised target image signal.

3. The method according to claim 2, characterized in that, Determining the frequency range of the actual noise signal in the mixed signal based on the amplitude distribution characteristics includes: The maximum and second maximum amplitude values ​​are determined based on the amplitude distribution characteristics of the mixed signal in the frequency domain. According to the preset frequency length, the frequency range including the second amplitude value is determined as the frequency range of the actual noise signal in the mixed signal.

4. The method according to claim 1, characterized in that, The method further includes: Obtain a model of the target nonlinear modulation system in a stable state; The step of adding a preset noise signal to the noisy image signal to obtain a mixed signal includes: The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model to obtain the mixed signal.

5. The method according to claim 4, characterized in that, The process of obtaining the target nonlinear modulation system model in a stable state includes: Establish an initial nonlinear modulation system model; Based on a preset first adjustment step size, the initial structural parameters of the initial nonlinear modulation system model are adjusted to obtain a target nonlinear modulation system model in a stable state.

6. The method according to claim 4, characterized in that, The step of inputting the image signal to be processed and the preset noise signal into the target nonlinear modulation system model to obtain the mixed signal includes: The image signal to be processed and the preset noise signal are input into the target nonlinear modulation system model; According to the preset second adjustment step size, the intensity of the preset noise signal in the target nonlinear modulation system model is adjusted sequentially to obtain multiple different mixed signals.

7. The method according to claim 1, characterized in that, The preset filtering conditions include one of the following: The signal-to-noise ratio of the mixed signal is greater than a preset threshold; The signal-to-noise ratio of the mixed signal reaches its maximum value; wherein, the signal-to-noise ratio reaches its maximum value when the relationship between the signal-to-noise ratio of the mixed signal and the intensity of the preset noise signal changes from positive to negative.

8. The method according to claim 1, characterized in that, The step of adding a preset noise signal to the noisy image signal to obtain a mixed signal includes: When the image signal to be processed is an image signal acquired when the light intensity is lower than the intensity threshold, a preset noise signal is added to the image signal to be processed to obtain a mixed signal.

9. An image processing apparatus, characterized in that, include: A noise-adding module is used to add a preset noise signal to a noisy image signal to obtain a mixed signal; the mixed signal includes multiple signals, and the multiple mixed signals are obtained by adding different preset noise signals to the image to be processed; The signal-to-noise ratio (SNR) determination module is used to determine the SNR of the mixed signal and to determine whether the SNR meets preset screening conditions. The denoising module is used to determine the actual noise signal in the mixed signal whose signal-to-noise ratio meets the preset screening conditions if the preset screening conditions are met, and to filter the actual noise signal to obtain the denoised target image signal.

10. An image processing device, characterized in that, include: A processor and a memory for storing executable instructions capable of running on the processor, wherein: When the processor is used to run the executable instructions, the executable instructions perform the steps of the method provided by any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the steps of the method provided in any one of claims 1 to 8.

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