Image super-resolution processing method and system based on WebGPU technology

Abstract

The invention provides an image super-division processing method and system based on a WebGPU technology, and the method comprises the following steps: S1, designing an image super-division model: designing the image super-division model through the combination of Transform and a convolutional neural network; s2, image super-division model training: offline training an image super-division model which is suitable for a WebGPU computing architecture and based on a lightweight neural network, and improving the reasoning performance and the memory utilization rate through model pruning, weight quantization and operator fusion for a resource-limited scene of a browser end; s3, constructing a front-end platform: constructing a front-end website by adopting a mainstream Web development framework; s4, model loading and reasoning process implementation; and S5, displaying and post-processing an output result. The method solves the problems that an existing image super-division model is low in computing resource utilization rate, poor in real-time performance, insufficient in platform compatibility and the like in the Web end deployment process.

Description

Technical Field

[0001] This invention belongs to the field of computer vision, specifically relating to an image super-resolution processing method and system based on WebGPU technology. Background Technology

[0002] In recent years, with the rapid development of artificial intelligence and computer vision technologies, image super-resolution (hereinafter referred to as super-resolution) models have been widely used in various fields such as image enhancement, image restoration, and video surveillance reconstruction. Existing image super-resolution models typically rely on local high-performance computing platforms or servers for inference, mainly using low-level interfaces such as CUDA to call local graphics card resources to achieve efficient model operation. However, these traditional methods require specially built high-performance hardware environments and cumbersome deployment processes, which are a significant burden on users and cannot meet their needs for using lightweight computer vision algorithm AI models directly in a browser.

[0003] While WebGL, as a previous-generation web-based graphics interface, achieved some GPU acceleration, it had limitations in parallel computing capabilities, bandwidth utilization, versatility, and security, making it difficult to support efficient inference for complex deep learning models. To address this, the W3C, in collaboration with Google, Hugging Face, and other organizations, developed the next-generation WebGPU standard (W3C WebGPU official standard draft: https: / / gpuweb.github.io / gpuweb / ). Based on modern GPU architecture, it boasts stronger resource scheduling capabilities, native support for compute shaders, higher asynchronous parallel efficiency, and more robust security mechanisms. WebGPU can bring near-native GPU acceleration performance to web applications, providing a foundation for browser-based inference of deep neural networks. Currently, WebGPU is gradually being supported by mainstream browsers, and its related APIs and shader languages ​​are maturing, providing strong technical support for deploying complex visual models on the web, improving real-time inference efficiency, and enriching online application scenarios.

[0004] However, the number of WebGPU applications remains limited, with some deployments on websites like Hugging Face. Furthermore, due to the rapid pace of standard updates, research on its applications is scarce. For reference, there are primarily introductions to WebGPU technology (https: / / dl.acm.org / doi / pdf / 10.1145 / 3532720.3535625) or comparisons of WebGL and WebGPU technologies (https: / / www.sciencedirect.com / science / article / pii / S1877050924006410). Efficiently migrating existing deep learning models to the WebGPU platform still faces challenges such as accuracy compatibility, cross-platform support, and browser compatibility.

[0005] Therefore, how to fully utilize the computing power of WebGPUs to achieve high-quality and high-performance image super-resolution inference on the browser side, and fill the data gaps in the WebGPU field, has become a key issue that the industry urgently needs to solve. Summary of the Invention

[0006] This invention provides an image super-resolution processing method and system based on WebGPU technology to solve the problems of low computing resource utilization, poor real-time performance, and insufficient platform compatibility of existing image super-resolution models when deployed on the Web.

[0007] The technical solution of the present invention is as follows: According to one aspect of the present invention, an image super-resolution processing method based on WebGPU technology is provided, comprising the following steps: S1. Image super-resolution model design: designing an image super-resolution model by combining Transformer and convolutional neural network; S2. Image super-resolution model training: offline training of an image super-resolution model based on lightweight neural network suitable for WebGPU computing architecture, and improving inference performance and memory utilization by model pruning, weight quantization and operator fusion for browser-side resource-constrained scenarios; S3. Front-end platform construction: building a front-end website using mainstream Web development frameworks; S4. Implementation of model loading and inference process; and S5. Output result display and post-processing.

[0008] Optionally, in the above-mentioned image super-resolution processing method based on WebGPU technology, in step S1, a hybrid network architecture combining the advantages of local feature extraction of convolutional neural networks and global self-attention mechanism of Transformer is adopted. By introducing multiple layers of Transformer modules and convolutional layers alternately stacked in the backbone network, the collaborative modeling of long-distance dependencies and fine local information in the image is realized. Furthermore, according to the parallelism and memory access requirements of the WebGPU platform, the computing units and feature connection methods in the hybrid structure are adjusted. In addition, an inference optimization module and a WebGPU capability detection module are introduced to optimize and dynamically adjust the inference process by detecting the resource capabilities of the user device, so as to maximize the utilization of device resources and model efficiency.

[0009] Optionally, in the above-mentioned image super-resolution processing method based on WebGPU technology, in step S2, the image super-resolution model is trained on a high-quality low-resolution image dataset. Combining the characteristics of the WebGPU platform, the image super-resolution model is subjected to structural pruning, weight quantization, and operator fusion optimization. After training, the image super-resolution model is exported as an ONNX or custom intermediate format compatible with web loading and inference, and a compression algorithm is used to optimize the weight file size.

[0010] Optionally, in the above-mentioned image super-resolution processing method based on WebGPU technology, in step S3, a WebGPU capability detection module is integrated to automatically identify the GPU type and supported features of the user device, and dynamically configure model inference resources according to the hardware detection results to achieve efficient operation compatible with multiple platforms.

[0011] Optionally, in the above image super-resolution processing method based on WebGPU technology, in step S4, the model resources are downloaded and initialized on the front-end page, and the buffer and texture resources required by WebGPU are allocated. The low-resolution image uploaded by the user is preprocessed by format conversion and normalization to meet the input requirements of the inference process.

[0012] Optionally, in the above image super-resolution processing method based on WebGPU technology, in step S5, the high-resolution image data generated by inference is converted into a browser-renderable format and displayed on the front-end page in real time.

[0013] According to another aspect of the present invention, an image super-resolution processing system based on WebGPU technology is provided, comprising: an image super-resolution module for performing super-resolution processing on an input image; an inference optimization module for optimizing the model super-resolution inference process; an image output module for post-processing and outputting the super-resolution image generated by the image super-resolution module, and formatting the high-resolution image data output by the model into a browser-renderable result as needed; a WebGPU capability detection module for automatically identifying the GPU type and supported features of the user device, dynamically configuring model inference resources according to the hardware detection results, and achieving efficient operation compatible with multiple platforms; a dynamic hardware detection and adaptive parameter selection module for adjusting the inference process in real time according to the GPU type, memory capacity, supported accuracy, etc. of the user device, so as to be compatible with different platforms and graphics card backends, and ensure inference effect and resource utilization; and a website front-end module for image uploading, previewing, and parameter configuration functions.

[0014] Optionally, in the aforementioned image super-resolution processing system based on WebGPU technology, the image super-resolution module utilizes a hybrid network architecture of convolutional neural networks and Transformer self-attention mechanisms to perform accurate medium- and long-range image modeling and generate high-resolution, high-quality images.

[0015] Optionally, in the above-mentioned image super-resolution processing system based on WebGPU technology, the inference optimization module utilizes efficient model weight loading, structured data flow management, and slice processing techniques to perform region partitioning and parallel computation on large-size images, effectively reducing memory usage and improving inference speed, ultimately supporting the realization of high-quality, lossless super-resolution image reconstruction.

[0016] The beneficial effects of the technical solution of the present invention are as follows: This invention presents an image super-resolution processing method and system based on WebGPU technology, overcoming the technical bottlenecks of traditional image super-resolution models in web-based deployments, such as low computational efficiency, poor real-time performance, and insufficient hardware compatibility. Compared to existing methods that rely on CPU or WebGL for inefficient inference, this invention can deeply utilize browser-side GPU resources. Through a specially optimized model structure and WebGPU interface, it significantly improves image processing speed and super-resolution reconstruction quality, achieving real-time output of high-resolution images. Leveraging the dynamic hardware detection and adaptive parameter adjustment of the WebGPU interface, this invention combines multi-platform compatibility and resource utilization advantages, enabling stable and efficient operation on different browsers and various GPU chips. This invention not only lowers the user threshold and operating costs but also significantly enhances the system's scalability, security, and practical application value. This method is particularly suitable for visual processing scenarios requiring real-time, convenient, high-quality processing and data security, such as online media editing and educational research, providing solid technical support and a superior user experience for the widespread application of image super-resolution models in the web ecosystem. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0018] Figure 1 This is a flowchart of the image super-resolution processing method based on WebGPU technology of the present invention. Detailed Implementation

[0019] To make the objectives, technical methods, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific examples. These examples are merely illustrative and not intended to limit the scope of the invention.

[0020] This invention combines the next-generation browser-side GPU acceleration capabilities of WebGPU to design a highly efficient image super-resolution network structure specifically adapted to the WebGPU architecture. This method can fully exploit the computational potential of mainstream GPUs in a multi-platform browser environment, performing high-quality super-resolution reconstruction of input images while significantly reducing latency and memory consumption, thus improving the user's real-time experience. This invention significantly improves image reconstruction quality and detail representation by fusing local convolutional features with a global self-attention mechanism. Furthermore, this invention supports dynamic hardware detection and adaptive parameter adjustment, is compatible with various browsers and backend GPU architectures, and possesses advantages in high scalability and easy deployment, providing a reliable technical foundation for the efficient application of image super-resolution models in web-based fields such as online editing and media processing.

[0021] The principle of this invention is: 1) For heterogeneous GPU environments on the web, an image super-resolution model structure and inference process adapted to the WebGPU interface standard were designed. The model undergoes structural optimization and accuracy degradation during the training and export phases to enable efficient allocation of GPU resources on the browser side. 2) An image super-resolution model structure integrating Transformer module and convolutional neural network is proposed. By combining global self-attention mechanism and local feature extraction capability, efficient collaborative modeling of image details and long-range dependencies is achieved, thereby improving the image reconstruction quality on the Web. 3) Through efficient model weight loading, structured data flow management and piecewise processing technology, high-quality super-resolution reconstruction of input images of any size is achieved. The high-resolution image data output by the model is formatted into browser-renderable results on demand, supporting application scenarios such as online media editing and real-time preview. 4) Introducing a dynamic hardware detection and adaptive parameter selection module, which adjusts the inference process in real time based on the user's device's GPU type, memory capacity, supported precision, etc., such as automatically selecting the workgroup size, data format and kernel distribution strategy, to ensure compatibility with different platforms and graphics card backends, and to guarantee inference performance and resource utilization.

[0022] like Figure 1 As shown, the image super-resolution processing method based on WebGPU technology of the present invention fully utilizes the parallel computing capabilities of WebGPU on the browser-side heterogeneous GPU resources to achieve high-quality super-resolution reconstruction of input images. The specific steps are as follows: S1. Image Super-Resolution Model Design. An image super-resolution model is designed by combining Transformer and Convolutional Neural Network. This model utilizes an attention mechanism to enhance super-resolution capabilities.

[0023] Specifically, in the structural design of the image super-resolution model, a hybrid network architecture combining the advantages of local feature extraction from convolutional neural networks with the global self-attention mechanism of Transformers is adopted. By introducing multiple layers of Transformer modules and convolutional layers alternately stacked in the backbone network, collaborative modeling of long-range dependencies and fine local information in images is achieved. Furthermore, based on the parallelism and memory access requirements of the WebGPU platform, the computational units and feature connection methods in the hybrid structure are adjusted to balance inference efficiency and reconstruction effect, thereby improving the model's image detail restoration capability and processing performance on the browser side. In addition, an inference optimization module and a WebGPU capability detection module are introduced to optimize and dynamically adjust the inference process by detecting the resource capabilities of the user's device, so as to maximize the utilization of device resources and model efficiency.

[0024] S2. Image super-resolution model training.

[0025] Offline training is used to develop lightweight neural network-based image super-resolution models suitable for WebGPU computing architectures. For resource-constrained browser scenarios, optimization techniques such as model pruning, weight quantization, and operator fusion are employed to improve inference performance and memory utilization.

[0026] Specifically, the image super-resolution model is trained on a high-quality low-to-high-resolution image dataset. Leveraging the characteristics of the WebGPU platform, optimizations such as structural pruning, weight quantization, and operator fusion are performed on the image super-resolution model. After training, the image super-resolution model is exported to an ONNX or custom intermediate format compatible with web loading and inference, and a compression algorithm is used to optimize the weight file size for rapid distribution and loading.

[0027] S3. Front-end Platform Setup. A mainstream web development framework is used to build the front-end website, enabling functions such as user image uploading, model parameter configuration, and processing result display. A WebGPU capability detection module is integrated to automatically identify the GPU type and supported features of the user's device, and dynamically configure model inference resources (such as workgroup size, data format, etc.) based on hardware detection results, achieving efficient operation compatible with multiple platforms.

[0028] S4. Model Loading and Inference Flow Implementation. The model resources are downloaded and initialized on the front-end page, and the necessary Buffer and Texture resources for the WebGPU are allocated. Low-resolution images uploaded by the user are preprocessed by format conversion and normalization to meet the input requirements of the inference flow.

[0029] S5. Output Result Display and Post-processing. The high-resolution image data generated by inference is converted into a browser-renderable format and displayed on the front-end page in real time. Users can download the resulting images and perform further editing or processing. The entire process integrates performance monitoring and anomaly feedback mechanisms, allowing users to view system operating status and error feedback.

[0030] The image super-resolution processing system based on WebGPU technology of the present invention includes: The image super-resolution module is used to perform super-resolution processing on the input image. It utilizes a hybrid network architecture of convolutional neural networks and Transformer self-attention mechanism to perform accurate mid-to-long-range image modeling and generate high-resolution, high-quality images.

[0031] The inference optimization module is used to optimize the super-resolution inference process of the model. Utilizing efficient model weight loading, structured data flow management, and piecewise processing techniques, it can perform region partitioning and parallel computation on large-size images, effectively reducing memory usage and improving inference speed, ultimately supporting high-quality, lossless super-resolution image reconstruction. The image output module is used for post-processing and outputting the super-resolution images generated by the super-resolution module. It formats the high-resolution image data output by the model into browser-renderable results as needed to support applications such as online media editing and real-time preview. The WebGPU capability detection module is used to automatically identify the GPU type and supported features of user devices, and dynamically configure model inference resources (such as workgroup size, data format, etc.) based on hardware detection results to achieve efficient operation compatible with multiple platforms. The dynamic hardware detection and adaptive parameter selection module adjusts the inference process in real time based on the user's device's GPU type, memory capacity, supported precision, etc., such as automatically selecting workgroup size, data format, and kernel distribution strategy to ensure compatibility with different platforms and GPU backends, guaranteeing inference performance and resource utilization; and The website front-end module is used for functions such as image uploading, previewing, and parameter configuration.

[0032] This invention utilizes the emerging WebGPU standard, enabling direct access to the underlying GPU for computation and rendering within web pages, achieving efficient and convenient image and video super-resolution functionality. It introduces a novel neural network structure, blending Transformer and convolutional structures, and employs an attention mechanism for upsampling, improving web-based model performance and ease of use compared to traditional WebGL and CPU computation. The implementation of this invention achieves efficient super-resolution processing of low-resolution images on the browser side through model training and structure optimization, front-end website platform construction, WebGPU inference process development, and efficient output management. This method not only fully leverages the parallel computing capabilities of mainstream GPUs, significantly improving the real-time performance and reconstruction quality of super-resolution inference, but also supports dynamic hardware detection and adaptive parameter adjustment, ensuring system compatibility and stability across multiple platforms and browsers. This invention can efficiently process large-size images, ensuring seamless output and user data security. The overall process possesses good scalability and ease of use, flexibly adapting to various application scenarios, providing a solid technical foundation for the widespread implementation of intelligent visual processing and image enhancement technologies on the web.

[0033] This invention proposes and implements an image super-resolution model system based on WebGPU technology, which can fully utilize the parallel computing advantages of GPUs on the browser side to achieve high-quality, low-latency image super-resolution inference. Unlike inefficient image processing techniques that rely solely on CPUs or traditional WebGL, this invention significantly improves the model's execution efficiency and platform adaptability on the web by using a deep neural network designed for the WebGPU architecture and optimized shader operators. The invention innovatively integrates a dynamic hardware detection and adaptive parameter adjustment mechanism, enabling the system to automatically adjust the inference process according to the GPU type and resource status of different devices, ensuring efficient and stable operation in multi-platform and multi-browser environments. This invention combines Transformer and Convolutional Neural Networks to improve the processing performance of large-size images, achieving efficient collaborative modeling of image details and long-range dependencies, and enhancing the model's perception of key regions. Based on these innovations, this invention significantly expands the web-based deployment capabilities of image super-resolution models in scenarios such as online media editing, image quality restoration, and lightweight AI computing, providing strong technical support for the popularization of intelligent vision processing and the improvement of user experience. As shown in Table 1, compared with existing methods, the method of the present invention significantly shortens the time for super-resolution of images to 4K resolution and significantly improves image processing speed, demonstrating the high efficiency of the method and system of the present invention.

[0034] Table 1. Comparison of time between the method of this invention and other browser-based super-resolution models when super-resolution is achieved to 4K resolution. The above description represents the preferred embodiment based on the inventive concept and working principle. The above embodiments should not be construed as limiting the scope of protection of these claims; other embodiments and combinations of implementations based on the inventive concept are all within the scope of protection of this invention.

Claims

1. An image super-resolution processing method based on WebGPU technology, characterized in that, Includes the following steps: S1. Image super-resolution model design: The image super-resolution model is designed by combining Transformer and convolutional neural network; S2. Image super-resolution model training: Offline training of the image super-resolution model based on a lightweight neural network suitable for WebGPU computing architecture. For resource-constrained scenarios on the browser side, inference performance and memory utilization are improved through model pruning, weight quantization and operator fusion. S3. Front-end Platform Setup: Build the front-end website using mainstream web development frameworks; S4. Model loading and inference process implementation; as well as S5. Output Results Display and Post-processing.

2. The image super-resolution processing method based on WebGPU technology according to claim 1, characterized in that, In step S1, a hybrid network architecture combining the advantages of local feature extraction from convolutional neural networks with the global self-attention mechanism of Transformers is adopted. By introducing multiple layers of Transformer modules and convolutional layers alternately stacked in the backbone network, the collaborative modeling of long-distance dependencies and fine local information in the image is achieved. According to the requirements of WebGPU platform for parallelism and memory access, the computing units and feature connection methods in the hybrid structure are adjusted. In addition, an inference optimization module and a WebGPU capability detection module are introduced to optimize and dynamically adjust the inference process by detecting the resource capabilities of the user's device, so as to maximize the utilization of device resources and model efficiency.

3. The image super-resolution processing method based on WebGPU technology according to claim 1, characterized in that, In step S2, the image super-resolution model is trained on a high-quality low-resolution image dataset. Taking into account the characteristics of the WebGPU platform, the image super-resolution model is optimized by structural pruning, weight quantization, and operator fusion. After training, the image super-resolution model is exported as an ONNX or custom intermediate format compatible with web loading and inference, and a compression algorithm is used to optimize the weight file size.

4. The image super-resolution processing method based on WebGPU technology according to claim 1, characterized in that, In step S3, a WebGPU capability detection module is integrated to automatically identify the GPU type and supported features of the user device, and dynamically configure model inference resources based on the hardware detection results to achieve efficient operation compatible with multiple platforms.

5. The image super-resolution processing method based on WebGPU technology according to claim 1, characterized in that, In step S4, the model resources are downloaded and initialized on the front-end page, and the Buffer and Texture resources required by WebGPU are allocated. The low-resolution images uploaded by the user are preprocessed by format conversion and normalization to meet the input requirements of the inference process.

6. The image super-resolution processing method based on WebGPU technology according to claim 1, characterized in that, In step S5, the high-resolution image data generated by inference is converted into a browser-renderable format and displayed on the front-end page in real time.

7. An image super-resolution processing system based on WebGPU technology, characterized in that, include: The image super-resolution module is used for super-resolution processing of the input image; The inference optimization module is used to optimize the super-resolution inference process of the model. The image output module is used to post-process and output the super-resolved image generated by the image super-resolution module, and to format the high-resolution image data output by the model into a browser-renderable result as needed. The WebGPU capability detection module is used to automatically identify the GPU type and supported features of user devices, and dynamically configure model inference resources based on hardware detection results to achieve efficient operation compatible with multiple platforms. The dynamic hardware detection and adaptive parameter selection module is used to adjust the inference process in real time according to the GPU type, memory capacity, and supported accuracy of the user device to ensure compatibility with different platforms and graphics card backends, and to guarantee inference performance and resource utilization. as well as The website's front-end module is used for image uploading, previewing, and parameter configuration.

8. The image super-resolution processing system based on WebGPU technology according to claim 7, characterized in that, The image super-resolution module utilizes a hybrid network architecture combining convolutional neural networks and Transformer self-attention mechanisms to perform accurate medium- and long-range image modeling, generating high-resolution, high-quality images.

9. The image super-resolution processing system based on WebGPU technology according to claim 7, characterized in that, The inference optimization module utilizes efficient model weight loading, structured data flow management, and piecewise processing techniques to perform region partitioning and parallel computation on large-size images, effectively reducing memory usage and improving inference speed, ultimately supporting high-quality, lossless super-resolution image reconstruction.

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