A card camera power consumption optimization method and system based on image processing

Abstract

The present invention provides a card camera power consumption optimization method and system based on image processing, which relates to the technical field of camera power consumption optimization. The method includes: obtaining a card camera lens light field; compressing the lens light field to reduce redundant data in the lens light field; constructing a photosensitive unit activation matrix of the card camera based on the compressed lens light field and a Kähler geometry algorithm; controlling the photosensitive units of the card camera through the photosensitive unit activation matrix to capture a real-scene image; performing a single conformal mapping on the real-scene image to denoise the real-scene image; encoding the denoised real-scene image with the constraint of minimizing the entropy production value representing the loss of image information to obtain a target real-scene image; and outputting the target real-scene image. The method ensures that data is compressed without losing image quality, and ultimately outputs a high-quality target image, thereby significantly reducing power consumption while maintaining image quality.

Description

Technical Field

[0001] The present invention relates to the technical field of camera power consumption optimization, and in particular to a card camera power consumption optimization method and system based on image processing. Background Art

[0002] A compact camera is a digital camera primarily used for everyday photography. Optimizing compact camera power consumption involves optimizing the camera's hardware and internal image processing logic to reduce power consumption during photography, image processing, and other operations. This typically involves optimizing the camera's sensor and processor, reducing unnecessary computation and power consumption, and extending the camera's battery life.

[0003] The need for optimizing compact camera power consumption stems from the widespread adoption of portable devices, which has led to higher demands for camera battery life. Traditional cameras, due to their computationally intensive nature and power-hungry sensors, have limited battery life. Rapid power consumption during extended shooting and high-resolution image processing can severely impact the user experience. Optimizing power consumption not only extends battery life but also improves device efficiency, enhancing user experience and satisfaction. Therefore, optimizing compact camera power consumption is not only a technological development requirement but also crucial for improving market competitiveness.

[0004] However, existing compact camera power consumption optimization mostly relies on reducing hardware power consumption, such as by adjusting the sensor sampling rate or using low-power mode. However, these methods often affect image quality, especially in the process of high-resolution and high dynamic range image processing. It is impossible to achieve efficient power consumption control while ensuring image quality. Summary of the Invention

[0005] In order to solve the technical problem that the power consumption optimization of card cameras in the existing technology mostly relies on reducing the power consumption of hardware, such as by adjusting the sampling rate of the sensor or using a low-power mode, but these methods often affect the image quality, especially in the process of high-resolution and high dynamic range image processing, it is impossible to achieve efficient power consumption control while ensuring image quality, the present invention provides a card camera power consumption optimization method and system based on image processing.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a card camera power consumption optimization method based on image processing, comprising:

[0009] S1: Get the light field of the card camera lens;

[0010] S2: Compression of the lens light field is performed in combination with the Gromov-Wasserstein distance to reduce redundant data in the lens light field;

[0011] S3: Based on the compressed lens light field, the activation matrix of the photosensitive unit of the card camera is constructed based on the Kähler geometry algorithm;

[0012] S4: Control the photosensitive unit of the card camera through the photosensitive unit activation matrix to capture the real scene image;

[0013] S5: performing single conformal mapping on the real scene image to denoise the real scene image;

[0014] S6: Encode the denoised real scene image under the constraint of minimizing the entropy production value representing the loss of image information to obtain the target real scene image;

[0015] S7: Output the target real scene image.

[0016] Second aspect

[0017] An embodiment of the present invention provides a card camera power consumption optimization system based on image processing, comprising:

[0018] processor;

[0019] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the card camera power consumption optimization method based on image processing as described in the first aspect is implemented.

[0020] The third aspect

[0021] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for optimizing power consumption of a compact camera based on image processing according to the first aspect is implemented.

[0022] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0023] In an embodiment of the present invention, first, the lens light field is acquired to provide complete optical information for subsequent image processing, ensuring the basic data quality for image processing. The lens light field is then compressed using the Gromov-Wasserstein distance, effectively reducing redundant data and reducing power consumption at the source, thus avoiding the problem of sacrificing image quality to save power in traditional methods. A Kähler geometry algorithm is then used to construct a photosensitive unit activation matrix, activating only the required photosensitive units, further reducing unnecessary calculations and power consumption. The real-life image captured by the card camera based on the photosensitive unit activation matrix is ​​then subjected to a single denoising operation using conformal mapping, which reduces the impact of noise and improves image quality while further saving power consumption. Finally, an optimized encoding method based on entropy minimization is used to ensure data compression without losing image quality, ultimately outputting a high-quality target image, effectively balancing power consumption and image quality. Image quality is maintained while significantly reducing power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic flow chart of a card camera power consumption optimization method based on image processing provided by an embodiment of the present invention;

[0026] Figure 2 A schematic structural diagram of a card camera power consumption optimization system based on image processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0028] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0029] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0030] Reference Manual Figure 1, shows a flow chart of a card camera power consumption optimization method based on image processing provided by an embodiment of the present invention.

[0031] An embodiment of the present invention provides a card camera power consumption optimization method based on image processing. This method can be implemented by a card camera power consumption optimization device based on image processing. The card camera power consumption optimization device based on image processing can be a terminal or a server. The processing flow of the card camera power consumption optimization method based on image processing can include the following steps:

[0032] S1: Get the light field of the card camera lens.

[0033] The lens light field is a four-dimensional data structure captured by a light field camera that describes the propagation and distribution of light in space. Specifically, the light field contains not only spatial position information but also the direction and angle of the light. It can be understood as a high-dimensional data model that describes the behavior of light in three-dimensional space. The light at each point is represented by two main parameters: the spatial position of the point (x, y, z) and the direction of the light propagation (usually expressed as an angle).

[0034] Alternatively, compact cameras can use specific light field sensors or multi-lens arrays to capture the position and direction of light in space, generating four-dimensional light field data. This data records the spatial coordinates and direction angle of each ray, forming a complete lens light field and providing rich optical information for subsequent image processing.

[0035] S2: Compression of the lens light field is performed in combination with the Gromov-Wasserstein distance to reduce redundant data in the lens light field.

[0036] The Gromov-Wasserstein distance (GW distance) is a mathematical tool used to measure the structural differences between two metric spaces. It minimizes the matching cost between the two spaces by considering the point sets in the two spaces and the mapping relationship between them. The core of the GW distance is to select the most appropriate matching scheme to maximize the distance metric between the two spaces. In image processing, the GW distance can help find the most representative light directions. This method compresses light field data and removes redundant light information, thereby reducing data size and power consumption.

[0037] It's important to note that compressing the lens light field using the Gromov-Wasserstein distance effectively reduces the amount of redundant data in the light field, thereby alleviating the computational burden of subsequent processing. This method selects the most representative light directions within the light field manifold, ensuring that only rays that contribute to image quality are retained, avoiding the transmission and processing of invalid information. This significantly reduces the total amount of data and power consumption while maintaining image quality.

[0038] In a possible implementation, S2 specifically includes:

[0039] S201: Obtain geometric measurements of light rays in the lens light field at different positions and directions, that is, probability distribution.

[0040] It's important to note that by quantifying the spatial distribution characteristics of light in the lens' light field, we can obtain the geometric measurements and probability distribution of light in four dimensions (position and direction). Specifically, a light field sensor can record the spatial coordinates (x, y, z) and propagation direction (angle parameters) of light. By statistically analyzing the density and directional distribution of light in each region, we can construct a probability density function for the light field manifold. Precisely quantifying the spatial distribution and directional information of light effectively reduces redundant data, providing high-quality foundational data for subsequent light field compression and power optimization, thereby improving camera processing efficiency and energy efficiency.

[0041] S202: Establishing a matching relationship between the lens light field and the reference lens light field in combination with geometric measurements, and outputting an optimal matching solution between the lens light field and the reference lens light field with the constraint of minimizing the matching cost between the lens light field and the reference lens light field.

[0042] The reference lens light field includes the sparse light structure that the design aims to preserve. This predefined sparse light field model encompasses the core optical structure. By filtering key light directions (such as those along object edges and textured areas), it forms a low-redundancy geometric template, guiding the compression process to retain the optical information that significantly impacts image quality.

[0043] The optimal matching solution is calculated as follows:

[0044] ;

[0045] ;

[0046] in, represents the optimal matching solution, Represent the light distribution in the lens light field and the reference lens light field respectively, Indicates compliance The light and the The set of matching schemes between the rays, Represents a matching scheme in a matching scheme set. Represents the lens light field rays Light field rays with reference lens The difference in light between Represented in manifold space The square of the norm between x and y is the matching cost, where the light difference includes the light position difference and the light direction difference. Indicates a matching scheme The entropy value of represents the regularization coefficient that controls the matching complexity, Indicates the matching scheme The x and y in the . Indicates taking the minimum function value .

[0047] Manifold space refers to a high-dimensional space commonly used in geometry and mathematical physics. It is a nonlinear space that partially resembles Euclidean space, where a point represents the position and direction of each ray in a light field. In light field processing, manifold space is used to describe the relationship between rays and how they vary across different light fields. The concept of manifold is crucial for understanding how light propagates and varies in three-dimensional or higher-dimensional spaces. Specifically, the norm in manifold space measures the difference between rays x and y in that space, including their spatial position and direction.

[0048] Optionally, the value range of λ can be adjusted between 0 and 1, such as 0.3 or 0.7.

[0049] It should be noted that, based on the Gromov-Wasserstein (GW) distance framework, the geometric structure differences between the lens light field and the reference light field are transformed into a matching cost optimization problem. The optimal matching solution is found by minimizing the matching cost (including position and orientation differences) and the entropy regularization term between the two light fields. The entropy term controls the ambiguity of the matching: the smaller the entropy value, the clearer the matching solution. The reference light field serves as a sparse structure template, guiding the algorithm to prioritize the preservation of critical light directions. Ultimately, by integrating optimal transmission theory with entropy regularization, structure-aware compression decisions are achieved. While maintaining the structural similarity between the original light field and the reference light field, overly complex matching relationships are suppressed, avoiding redundant computations. This directly reduces the search space and memory usage of the matching algorithm during the compression phase, reducing the amount of light field data. Through sparse mapping, a low-power foundation is laid for the subsequent selective activation of photosensitive units.

[0050] S203: Mapping the lens light field to the reference lens light field according to the optimal matching solution to compress the lens light field.

[0051] Based on the optimal matching scheme, the original light field is mapped to the sparse manifold space of the reference light field. Geometric compression of the light field data is achieved by removing redundant rays (e.g., rays that deviate significantly from the reference structure) and retaining core rays with low matching costs. This process reduces the data volume while ensuring that the geometric characteristics of the compressed light field are consistent with the design objectives.

[0052] It should be noted that by mapping the lens light field to the sparse manifold space of the reference light field, redundant light is eliminated and the core light is retained, thereby effectively reducing the amount of data and the computational burden. At the same time, it ensures that the geometric characteristics of the compressed light field are consistent with the design goals, which helps to optimize the power consumption and processing efficiency of the compact camera.

[0053] S3: Based on the compressed lens light field, the activation matrix of the photosensitive unit of the card camera is constructed based on the Kähler geometry algorithm.

[0054] Kähler geometry is an algorithm used in complex geometry, primarily for describing and analyzing geometric spaces with certain symmetries and structures. In image processing, Kähler geometry can be used to construct mathematical models of light fields, optimizing photoreceptor activation strategies by studying the local variations and propagation of light fields. The photoreceptor activation matrix indicates whether a camera's photoreceptors need to be activated, with each element representing the activation state of a photoreceptor. The activation matrix constructed using Kähler geometry can precisely determine which photoreceptors need to be activated and which can remain deactivated based on the compressed lens light field, thereby reducing unnecessary power consumption and computation.

[0055] It's important to note that the compressed lens light field is analyzed and processed using a Kähler geometry algorithm to construct a photosensitive cell activation matrix. This matrix precisely determines which photosensitive cells should be activated to capture valid image information and which can be disabled to reduce power consumption. This process significantly reduces unnecessary computation and power consumption while maintaining image quality, thereby optimizing the camera's overall energy efficiency.

[0056] In a possible implementation, S3 specifically includes:

[0057] S301: Establishing propagation functions of the compressed lens light field at different positions on the photosensitive plane.

[0058] The specific propagation function is:

[0059] ;

[0060] in, represents pi, represents a natural constant, z represents a complex position point on the photosensitive plane, n represents the sub-beam number in the compressed lens light field, and i represents a negative unit. represents the Gaussian envelope factor related to n, represents the negative exponential term describing the change of the light field related to i, n and z, Represents the propagation function value of the compressed lens light field at position z.

[0061] The propagation function is a superposition of complex waves that simulates the diffusion, interference, and modulation of light in space. It provides information about the light wave propagation at each pixel location.

[0062] S302: Based on the propagation function, the activation intensity potential energy field of the photosensitive units at different positions is constructed using the Kähler geometry algorithm.

[0063] The activation intensity potential field is calculated as follows:

[0064] ;

[0065] in, It represents the propagation function value of the compressed lens light field at position z, that is, the propagation intensity. represents the activation intensity potential field describing the local changes in the compressed lens light field at each position z, Indicates partial derivative, log indicates logarithmic function, z0 indicates visual focus, represents the complex conjugate of z0, Indicates taking z and The complex real part of the complex inner product between Indicates the adjustment coefficient that controls the degree of influence of visual focus on the activation intensity potential energy field, and Represents z and Find the partial derivative, represents the complex conjugate of z.

[0066] The Kähler geometry algorithm is a mathematical method widely used in complex geometric spaces, particularly when studying geometric bodies with certain symmetries. In image processing, it is used to describe and calculate the propagation characteristics and local variations in the light field, optimizing the activation of photoreceptors through geometric principles. The activation intensity potential field is calculated based on the propagation function (light field propagation) and describes the activation intensity of photoreceptors at different locations. This field uses the derivatives of the propagation function and geometric relationships to help determine the local variations of photoreceptors, specifically which photoreceptors should be activated. This field incorporates the influence of visual focus and adjusts the activation intensity at different locations using a regularization coefficient, thereby optimizing the image acquisition process. Using the Kähler geometry algorithm, the activation intensity potential field of photoreceptors is precisely calculated, ensuring that only required photoreceptors are activated, reducing unnecessary computation and energy consumption. This significantly reduces power consumption while optimizing image quality, improving overall camera energy efficiency.

[0067] Optionally, an adjustment coefficient controls the influence of the visual focus on the activation intensity potential field. Specifically, it adjusts the effect of the complex inner product between the visual focus and other locations on the activation intensity, thereby determining which photoreceptors are activated. By varying the value of the adjustment coefficient, the importance of the focus in photoreceptor activation can be adjusted. For example, values ​​of 0.3 or 0.6 can be used.

[0068] S303: Convert the activation intensity potential energy field into a photosensitive unit activation matrix that describes whether each photosensitive unit is activated.

[0069] The photosensitive unit activation matrix is ​​specifically:

[0070] ;

[0071] in, Represents the photosensitive unit activation matrix.

[0072] It should be noted that refined control of photosensitive cells is achieved by utilizing propagation functions and Kähler geometry algorithms to accurately calculate the activation intensity potential field of the photosensitive cells. This method activates only those photosensitive cells that contribute to image quality, avoiding unnecessary calculations and power consumption. This significantly reduces power consumption and improves the camera's energy efficiency and performance while ensuring efficient image acquisition. This activation control method, based on light field propagation and geometric optimization, further improves the accuracy of power optimization.

[0073] S4: Control the photosensitive unit of the card camera through the photosensitive unit activation matrix to collect real-scene images.

[0074] As you can see, the use of a photosensitive cell activation matrix controls the camera's photosensitive cells, ensuring that only the necessary ones are activated to capture real-world images. This precise control prevents the activation of ineffective photosensitive cells, reduces power consumption, and ensures that the captured image information is complete and efficient. This approach optimizes camera performance and reduces unnecessary computation and power consumption.

[0075] In a possible implementation, S4 is specifically:

[0076] Determine whether the photosensitive unit activation element value in the photosensitive unit activation matrix is ​​greater than the preset photosensitive unit activation element value. If so, turn on the photosensitive unit corresponding to the photosensitive unit activation element value; otherwise, turn off the photosensitive unit corresponding to the photosensitive unit activation element value.

[0077] It should be noted that those skilled in the art can set the size of the preset photosensitive unit activation element value according to actual needs, and the present invention does not limit this.

[0078] It can be understood that the element value of the photosensitive unit activation matrix is ​​compared with a preset threshold value. If the element value is greater than the threshold value, the corresponding photosensitive unit is activated. Otherwise, the photosensitive unit is turned off. In this way, the switching of the photosensitive units can be precisely controlled, the image acquisition process can be optimized, and unnecessary power consumption can be reduced.

[0079] S5: Performing single conformal mapping on the real scene image to denoise the real scene image.

[0080] It's important to note that conformal mapping is a denoising method that preserves image geometry. It adjusts the image's local geometry to preserve detail and structure while effectively removing unnecessary noise. Single-pass conformal mapping preserves the image's geometry, transforming the complex denoising process into a simple adjustment of the local geometry. This avoids the redundant multi-step iterations or high-dimensional computations required in traditional methods. This not only improves image quality but also avoids excessive computation and resource consumption, thereby optimizing power consumption.

[0081] In a possible implementation, S5 specifically includes:

[0082] S501: Construct the Weyl tensor of the real scene image.

[0083] The Weyl tensor is a mathematical tool for describing changes in curvature in space or images and can be used for geometric analysis of curvature or deformation. Specifically, the process for constructing a Weyl tensor for a real-world image is as follows: Calculating local image curvature: First, the curvature of local geometric features (such as edges and textures) is calculated. Through derivative operations, the changes in each pixel in different directions can be obtained, reflecting the local curvature or deformation of the image. Differential geometry methods are then used to analyze the curvature and deformation of the image. The Weyl tensor helps quantify the geometric changes in different regions of the image. This process is primarily achieved by calculating metric tensors and differential operators related to local image changes. Perturbation information is then extracted. The main information captured by the Weyl tensor is geometric perturbations in the image (such as local brightness changes and morphological deformation). This perturbation information helps identify and suppress noise during denoising while preserving image structure and details. Finally, a Weyl tensor describing the local geometric perturbations of the image is generated using mathematical formulas such as the Hessian matrix or the Riemann tensor. This tensor reflects the local curvature changes of the image and provides accurate geometric data for subsequent denoising and image optimization.

[0084] S502: With the goal of minimizing the difference between the Frobenius norm of the Weyl tensor and the peak signal-to-noise ratio of the real image, determine the denoising optimization objective function of the real image to obtain a Weyl tensor transformation operator that describes the local geometric perturbation characteristics of the real image.

[0085] The denoising optimization objective function is specifically:

[0086] ;

[0087] in, Represents the Weyl tensor transformation operator that describes the local geometric perturbation characteristics of the real scene image. represents the Weyl tensor, express The square of the Frobenius norm, Represents a real-life image. Indicates passing The real scene image after mapping, represents the reference image, Indicates taking the minimum function value , express and The peak signal-to-noise ratio between Indicates the regularization factor that adjusts the impact of peak signal-to-noise ratio.

[0088] Tensor transformation operators refer to mathematical operations applied to images. They describe how to geometrically transform or adjust a real-world image using the Weyl tensor to remove noise. A reference image is an ideal or target image that serves as a comparison standard during the denoising process. In denoising optimization, the reference image is used to evaluate the quality of the denoised image. The denoising effect is determined by calculating the peak signal-to-noise ratio (PSNR) between the two. The PSNR reflects the similarity between the denoised image and the reference image, thus guiding the optimization process.

[0089] It should be noted that the denoising optimization objective function is constructed by minimizing the difference between the Frobenius norm of the Weyl tensor and the peak signal-to-noise ratio of the image. This optimization objective considers the balance between geometric perturbations of the image and noise removal. Specifically, by minimizing the Frobenius norm of the Weyl tensor, geometric distortion of the image can be reduced, while image quality is preserved by adjusting the difference between the PSNR and the reference image. The core of this process is to find the optimal Weyl tensor transformation operator that removes noise through precise geometric adjustments while preserving image detail and structure.

[0090] S503: Based on the obtained Weyl tensor transformation operator, perform single conformal mapping on the real scene image to denoise the real scene image.

[0091] Specifically, the advantage of this process is that by constructing a Weyl tensor and combining it with the denoising optimization objective function, the geometric perturbation characteristics of the image can be precisely controlled, thereby achieving efficient denoising. Specifically, the Weyl tensor transformation operator can effectively describe the local geometric changes in the image, ensuring that the image retains important details while denoising. Image quality is optimized by minimizing the difference between the Frobenius norm and the peak signal-to-noise ratio of the Weyl tensor. In addition, this process utilizes conformal mapping, which maintains the geometric structure of the image while simplifying the image transformation process. Specifically, conformal mapping is a transformation method that removes unnecessary complex geometric deformations and noise from the image by keeping the angle unchanged, reducing the amount of computation and power consumption, avoiding excessive calculation of redundant information, improving the efficiency of power optimization, and ensuring high-quality image output.

[0092] S6: Encode the denoised real scene image with the constraint of minimizing the entropy production value representing the loss of image information to obtain the target real scene image.

[0093] Entropy production refers to the degree of information loss during the image encoding process. Entropy is a measure of information quantity; lower entropy production indicates less information loss. In image processing, minimizing entropy production means preserving as much useful information as possible during image encoding, minimizing compression losses. By constraining the encoding strategy to minimize entropy production, image quality is maintained while reducing the additional computational load caused by redundant data processing (such as repeated correction of high-frequency information or high-complexity compression). This reduces chip operation frequency and memory accesses, directly reducing the power consumption of the encoding module. This also avoids the need for subsequent correction operations (such as recompression or frame interpolation) caused by information loss, further optimizing the power consumption of compact cameras.

[0094] In a possible implementation, S6 specifically includes:

[0095] S601: Calculate the image information entropy of the denoised real scene image.

[0096] S602: Establishing an evolution model of image information entropy in a metric space to simulate the diffusion process of image information of the denoised real scene image over time.

[0097] A metric space is a mathematical concept in which the distance or similarity between elements in a space can be defined using a metric (a distance function). In image processing, metric spaces are often used to represent the structure and information content of an image. Distance functions can measure the differences between different image regions or pixels. Metric spaces can be Euclidean, Manhattan, Hamming, or LP spaces.

[0098] The evolution model is used to describe the changing process of image information. Specifically, this evolution model simulates the change of image information entropy over time through the diffusion equation, aiming to optimize the image denoising process through temporal evolution.

[0099] The specific evolution model is:

[0100] ;

[0101] in, Indicates partial derivative, t indicates time variable, Represented in metric space The information entropy diffusion Laplace operator in, represents the gradient of information entropy H, Represented in metric space Next The square of the norm of Representing a metric space The Ricci curvature tensor of .

[0102] The information entropy diffusion Laplace operator is a Laplace operator defined in metric space and applied to information entropy. It measures the diffusion and rate of change of image information. In the context of image processing, the Laplace operator helps describe how information diffuses from one region to another. The diffusion process is related to the local structure and texture of the image, as well as its relationship with the surrounding area. Using this operator can simulate how information propagates smoothly across an image and reduce noise. The Ricci curvature tensor is a tensor that describes the geometric properties of a manifold. It measures the influence of the local curvature of the manifold on the image structure in metric space. Specifically, the Ricci curvature tensor reflects the degree of curvature of the image structure in different directions. By incorporating Ricci curvature, the evolutionary model can capture differences in the local geometric features of the image and further optimize the effects of image processing.

[0103] The advantage of this evolutionary model lies in its comprehensive consideration of the image's geometric structure and information diffusion. By influencing the information entropy's diffusion Laplacian operator and the Ricci curvature tensor, it can precisely control the denoising process while preserving the image's structural information. This approach not only removes noise but also enhances the propagation of useful information within the image, effectively improving image quality.

[0104] S603: Solve the steady-state solution of the evolution model to obtain the optimal entropy distribution map of the real scene image under the minimum entropy production value.

[0105] S604: Encode the denoised real scene image based on the optimal entropy distribution map to obtain a target real scene image.

[0106] In a possible implementation, the encoding method in S604 specifically includes: lossy compression encoding and lossless compression encoding, wherein the lossy compression encoding includes JPEG and WebP, and the lossless compression encoding includes PNG, GIF, and TIFF.

[0107] In a possible implementation, S604 specifically includes:

[0108] S6041: Sort the information entropy of pixels at different positions in the denoised real scene image according to the optimal entropy distribution map.

[0109] S6042: Using the upper limit of the bit rate of the compact camera as a constraint, allocate the bit rate in a manner inversely proportional to the pixel information entropy value to encode the real scene image.

[0110] Specifically, the information entropy of pixels at different locations in the denoised image is first sorted according to the optimal entropy distribution map to determine which areas contain the most information. Next, within the camera's bitrate limit, the bitrate is allocated inversely proportional to the pixel entropy values. Regions with high information content are allocated a higher bitrate (a first preset bitrate), ensuring higher-quality encoding of important details, while regions with less information use a lower bitrate (a second preset bitrate). This effectively compresses data while maintaining image quality. The first preset bitrate is greater than the second preset bitrate. The specific bitrate can be adjusted based on actual needs.

[0111] Specifically, the image denoising effect can be optimized by calculating image information entropy and utilizing an evolution model in metric space. First, the entropy of the denoised image is calculated to measure the amount of information in the image. Then, a model for the temporal evolution of information entropy is established to simulate the diffusion of image information. The diffusion equation is then used to dynamically optimize image detail and noise. Next, the steady-state solution of this equation is solved to obtain the optimal image entropy distribution, ensuring maximum information retention and minimization of irrelevant noise. Finally, the image is encoded according to the optimal entropy distribution map to obtain the target real-world image, ultimately optimizing image quality and reducing noise.

[0112] S7: Output the target real scene image.

[0113] In practical applications, this solution achieves high-efficiency imaging and energy control through multi-stage collaborative processing. First, a light field sensor captures four-dimensional light field data, and structure-aware compression is performed using the Gromov-Wasserstein distance. By matching the optimal mapping of the light field manifold, redundant light directions are eliminated, reducing data volume. Subsequently, a photosensitive cell activation matrix is ​​constructed based on the Kähler geometry algorithm. Its symmetry and complex structure precisely control the local response of the CMOS sensor, activating only photosensitive cells in critical areas to reduce circuit power consumption. During the image acquisition phase, non-essential photosensitive cells are dynamically deactivated to reduce dark current noise and computational load. Next, single-shot conformal mapping is used for image denoising, eliminating high-frequency noise by preserving local angular relationships. This reduces computational iterations compared to traditional filtering algorithms and further optimizes camera power consumption. Finally, encoding optimization is performed using entropy production as a constraint, combined with dynamic code length allocation to suppress blocking artifacts and frequency domain distortion. This compression process reduces memory access frequency while avoiding recompression operations caused by information loss, ultimately reducing the computational frequency and power consumption of compact camera chips. The entire process achieves a balanced optimization of image quality and energy efficiency through the synergistic effect of light field compression, selective photosensitive activation, geometric shape-preserving denoising and entropy-constrained coding.

[0114] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0115] In an embodiment of the present invention, first, the lens light field is acquired to provide complete optical information for subsequent image processing, ensuring the basic data quality for image processing. The lens light field is then compressed using the Gromov-Wasserstein distance, effectively reducing redundant data and reducing power consumption at the source, thus avoiding the problem of sacrificing image quality to save power in traditional methods. A Kähler geometry algorithm is then used to construct a photosensitive unit activation matrix, activating only the required photosensitive units, further reducing unnecessary calculations and power consumption. The real-life image captured by the card camera based on the photosensitive unit activation matrix is ​​then subjected to a single denoising operation using conformal mapping, which reduces the impact of noise and improves image quality while further saving power consumption. Finally, an optimized encoding method based on entropy minimization is used to ensure data compression without losing image quality, ultimately outputting a high-quality target image, effectively balancing power consumption and image quality. Image quality is maintained while significantly reducing power consumption.

[0116] Reference Manual Figure 2 , shows a structural schematic diagram of a card camera power consumption optimization system based on image processing provided by the present invention.

[0117] The present invention further provides a card camera power consumption optimization system 20 based on image processing, which is applied to the above-mentioned card camera power consumption optimization method based on image processing, comprising:

[0118] Processor 201.

[0119] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201 , the method for optimizing the power consumption of a card camera based on image processing according to the method embodiment is implemented.

[0120] The card camera power consumption optimization system 20 based on image processing provided by the present invention can execute the above-mentioned card camera power consumption optimization method based on image processing and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on them.

[0121] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0122] In an embodiment of the present invention, first, the lens light field is acquired to provide complete optical information for subsequent image processing, ensuring the basic data quality for image processing. The lens light field is then compressed using the Gromov-Wasserstein distance, effectively reducing redundant data and reducing power consumption at the source, thus avoiding the problem of sacrificing image quality to save power in traditional methods. A Kähler geometry algorithm is then used to construct a photosensitive unit activation matrix, activating only the required photosensitive units, further reducing unnecessary calculations and power consumption. The real-life image captured by the card camera based on the photosensitive unit activation matrix is ​​then subjected to a single denoising operation using conformal mapping, which reduces the impact of noise and improves image quality while further saving power consumption. Finally, an optimized encoding method based on entropy minimization is used to ensure data compression without losing image quality, ultimately outputting a high-quality target image, effectively balancing power consumption and image quality. Image quality is maintained while significantly reducing power consumption.

[0123] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0124] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0125] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0126] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0127] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0128] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0129] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0130] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0132] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for optimizing power consumption of a card camera based on image processing according to the method embodiment is implemented.

[0136] The computer-readable storage medium provided by the present invention can implement the steps and effects of the card camera power consumption optimization method based on image processing of the above method embodiment. To avoid repetition, the present invention will not elaborate on them.

[0137] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0138] In an embodiment of the present invention, first, the lens light field is acquired to provide complete optical information for subsequent image processing, ensuring the basic data quality for image processing. The lens light field is then compressed using the Gromov-Wasserstein distance, effectively reducing redundant data and reducing power consumption at the source, thus avoiding the problem of sacrificing image quality to save power in traditional methods. A Kähler geometry algorithm is then used to construct a photosensitive unit activation matrix, activating only the required photosensitive units, further reducing unnecessary calculations and power consumption. The real-life image captured by the card camera based on the photosensitive unit activation matrix is ​​then subjected to a single denoising operation using conformal mapping, which reduces the impact of noise and improves image quality while further saving power consumption. Finally, an optimized encoding method based on entropy minimization is used to ensure data compression without losing image quality, ultimately outputting a high-quality target image, effectively balancing power consumption and image quality. Image quality is maintained while significantly reducing power consumption.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0140] There are a few points to note:

[0141] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0142] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly on" or "under" the other element or intervening elements may be present.

[0143] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0144] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A card camera power consumption optimization method based on image processing, characterized in that: include: S1: Acquire the lens light field of the card camera; S2: compressing the lens light field to reduce redundant data of the lens light field; S3: constructing a photosensitive unit activation matrix of the card camera based on the compressed lens light field and the Keller geometry algorithm; S4: controlling the photosensitive units of the card camera through the photosensitive unit activation matrix to capture a real scene image; S5: performing single conformal mapping on the real scene image to denoise the real scene image; S6: Encode the denoised real scene image under the constraint of minimizing the entropy production value representing the loss of image information to obtain the target real scene image; S7: Outputting the target real scene image; Wherein, in step S2, the lens light field is compressed in combination with the Gromov-Wasserstein distance to reduce redundant data of the lens light field; The S2 specifically includes: S201: Obtaining geometric measurements, i.e., probability distributions, of light rays in the lens light field at different positions and directions; S202: Establishing a matching relationship between the lens light field and a reference lens light field based on the geometric measurement, and outputting an optimal matching solution between the lens light field and the reference lens light field with minimizing the matching cost between the lens light field and the reference lens light field as a constraint; S203: Mapping the lens light field to the reference lens light field according to the optimal matching solution to compress the lens light field.

2. The card camera power consumption optimization method based on image processing according to claim 1, characterized in that: The S3 specifically includes: S301: Establishing propagation functions of the compressed lens light field at different positions on the photosensitive plane; S302: constructing activation intensity potential energy fields of photosensitive units at different positions according to the propagation function using the Kähler geometry algorithm; S303: Convert the activation intensity potential energy field into the photosensitive unit activation matrix that describes whether each photosensitive unit is activated.

3. The card camera power consumption optimization method based on image processing according to claim 1, characterized in that: The S4 is specifically: Determine whether the photosensitive unit activation element value in the photosensitive unit activation matrix is ​​greater than a preset photosensitive unit activation element value. If so, turn on the photosensitive unit corresponding to the photosensitive unit activation element value; otherwise, turn off the photosensitive unit corresponding to the photosensitive unit activation element value.

4. The card camera power consumption optimization method based on image processing according to claim 1, characterized in that: The S5 specifically includes: S501: Constructing the Weyl tensor of the real scene image; S502: determining a denoising optimization objective function for the real scene image with the goal of minimizing the difference between the Frobenius norm of the Weyl tensor and the peak signal-to-noise ratio of the real scene image, so as to obtain a Weyl tensor transformation operator that describes local geometric perturbation characteristics of the real scene image; S503: Performing single conformal mapping on the real scene image based on the acquired Weyl tensor transformation operator to denoise the real scene image.

5. The card camera power consumption optimization method based on image processing according to claim 1, characterized in that: The S6 specifically includes: S601: Calculating the image information entropy of the denoised real scene image; S602: Establishing an evolution model of the image information entropy in a metric space to simulate the diffusion process of the image information of the denoised real scene image over time; S603: solving the steady-state solution of the evolution model to obtain an optimal entropy distribution map of the real scene image under the minimum entropy production value; S604: Encode the denoised real scene image based on the optimal entropy distribution map to obtain the target real scene image.

6. The card camera power consumption optimization method based on image processing according to claim 5, characterized in that: The encoding method in S604 specifically includes: lossy compression encoding and lossless compression encoding, wherein the lossy compression encoding includes JPEG and WebP, and the lossless compression encoding includes PNG, GIF and TIFF.

7. The card camera power consumption optimization method based on image processing according to claim 5, characterized in that: The S604 specifically includes: S6041: Sort the information entropy of pixels at different positions in the denoised real scene image according to the optimal entropy distribution map; S6042: Using the upper limit of the bit rate of the card camera as a constraint, allocating the bit rate in a manner inversely proportional to the pixel information entropy value to encode the real scene image.

8. A card camera power consumption optimization system based on image processing, characterized in that: include: processor; A memory storing computer-readable instructions, wherein the computer-readable instructions, when executed by the processor, implement the card camera power consumption optimization method based on image processing according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the card camera power consumption optimization method based on image processing according to any one of claims 1 to 7 is implemented.

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