MicroAlgo develops quantum image edge extraction algorithm for noisy images

SHENZHEN, China, May 20, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today that it has proposed a quantum image edge extraction algorithm for noisy images, which is an innovative achievement of the deep integration of quantum computing and digital image processing. The algorithm takes quantum state encoding as its core, mapping the grayscale and position information of image pixels to quantum superposition states, thereby realizing parallel storage and processing of massive information.

The core innovation of the quantum image edge extraction algorithm for noisy images lies in the dual quantum space filter and adaptive threshold non-maximum suppression: the former constructs two correlated quantum filtering spaces to perform targeted suppression of statistical noise and impulse noise respectively, while utilizing quantum entanglement characteristics to achieve information linkage and avoid edge blurring; the latter automatically generates thresholds adapted to image features through quantum operations, enabling precise screening of edge points without manual intervention. The entire process is driven by quantum operation circuits, breaking through the efficiency and accuracy bottlenecks of classical algorithms and providing a revolutionary solution for edge extraction in complex noisy environments.

Quantum State Encoding: Quantumization of Image Information

The noisy image to be processed first undergoes quantum state encoding, converting the grayscale values and position coordinates of pixels into quantum superposition states. For example, the pixel values of an 8-bit grayscale image can be encoded into the superposition states of qubits, while the position information is associated with the corresponding grayscale states through quantum entanglement. This process simultaneously preserves the noise characteristics and gradient information of the image, providing a complete data foundation for subsequent processing and avoiding information loss caused by multi-step conversions in classical algorithms.

Dual Quantum Space Filtering: Noise Suppression and Detail Preservation

The encoded quantum image enters the dual quantum space filter. The first quantum space targets statistical noise such as Gaussian noise, suppressing noise through smoothing operations on quantum states while preserving edge regions; the second quantum space utilizes quantum entanglement characteristics to accurately locate and filter impulse noise, while ensuring that edge details are not mistakenly deleted through an information linkage mechanism. For example, in medical imaging, this filter can simultaneously remove statistical noise introduced by low-dose scanning and device impulse interference, while completely preserving the edge contours of tiny lesions.

Gradient Calculation and Direction Analysis

The filtered quantum image enters the gradient calculation module, which rapidly computes the grayscale gradient magnitude and direction of each pixel through quantum parallel operations. The superposition property of quantum states enables the gradient calculation of the entire image to be completed synchronously, with efficiency far exceeding the point-by-point scanning of classical algorithms. For example, in remote sensing image processing, this module can accurately capture the gradient features of terrain changes, providing a basis for subsequent edge localization.

Non-Maximum Suppression: Edge Refinement

After gradient calculation is completed, the algorithm performs non-maximum suppression in the quantum domain, comparing the magnitudes of adjacent pixels along the gradient direction and retaining only local maximum points, thereby thinning wide edges into single-pixel width. Quantum parallel computing ensures that this process is globally synchronized, avoiding edge breakage caused by point-by-point processing in classical algorithms. For example, in industrial defect detection, this module can accurately extract tiny crack edges on the surface of workpieces, ensuring continuity.

Adaptive Threshold Screening: Edge Classification

The algorithm automatically analyzes the grayscale distribution and noise characteristics of the image through quantum operations to generate dynamic thresholds, classifying edge points into three categories: strong edges, weak edges, and non-edges. Weak edges are often ignored in classical algorithms, but quantum adaptive thresholds can determine whether they are real edges by combining contextual information. For example, in autonomous driving scenarios, this module can capture weak edges of road markings under rainy or foggy weather, improving the robustness of environmental perception.

Edge Connection and Decoding Output

Finally, the algorithm connects broken edge segments through quantum operations and decodes the quantum state results into classical image format. The quantum parallel architecture ensures that this process is completed efficiently, enabling real-time processing even for high-resolution images (such as 4K remote sensing imagery).

MicroAlgo's quantum image edge extraction algorithm for noisy images possesses significant advantages. It adopts advanced algorithm models and intelligent architecture, with extremely high computational efficiency, capable of processing massive amounts of data in a short time. Its precision far exceeds that of similar technologies, and it has strong stability, enabling it to adapt to complex and changing environments while effectively reducing the failure rate. At the same time, it features excellent compatibility, seamlessly integrating with various systems and greatly reducing integration costs. In terms of application scope, it is extremely wide-ranging. In the industrial field, it can assist intelligent manufacturing, realize automated monitoring and optimization of production processes, and improve production efficiency and product quality. In the medical industry, it can assist disease diagnosis by analyzing medical imaging and other data, providing doctors with precise references. In the financial field, it can be used for risk assessment and prediction to safeguard fund security. In the transportation field, it can optimize traffic flow, improve travel efficiency, and provide strong support for urban intelligent traffic management.

With the development of quantum computing, the industrialization process of quantum image processing technology continues to accelerate. MicroAlgo will continue to optimize this quantum edge extraction algorithm for noisy images, improve the adaptability of quantum operation circuits, expand application boundaries, promote the deep integration of the algorithm with quantum hardware, and assist in the quantum upgrade of image processing technology and the intelligent development of the entire industry.