In the era of big data and artificial intelligence，image processing has emerged as a cornerstone technology that drives advancements across diverse fields，including medical diagnostics，autonomous systems，and remote sensing. The exponential growth of high-resolution image data has created unprecedented computational demands that classical computing architectures increasingly struggle to meet. As the progress under Moore’s law slows，quantum computing has emerged as a transformative paradigm； it leverages fundamental quantum properties，including superposition，entanglement，and quantum parallelism，to overcome classical limitations. This convergence has given rise to the rapidly evolving field of quantum image processing （QIP），which promises to redefine the boundaries of computational imaging capabilities. This comprehensive survey provides a systematic analysis of QIP’s remarkable evolution over the past five years，documenting its transition from a theoretical concept to an emerging practical technology. We introduce an integrated classification framework that mirrors the complete image processing pipeline——from fundamental representation to advanced applications——thus creating a coherent perspective that effectively connects quantum mechanical principles with practical implementation requirements. The foundation of this framework lies in quantum image representation models，where innovative schemes，such as novel enhanced quantum repre-sentation，flexible representation of quantum images，and generalized quantum image representation，have advanced to support arbitrary image dimensions and complex color spaces while simultaneously optimizing critical qubit utilization efficiency. Building upon this foundation，QIP techniques demonstrate increasingly sophisticated capabilities. Advanced filtering algorithms achieve exponential speedups in noise reduction tasks，and quantum Fourier and wavelet transforms enable unprecedented efficiency in image compression. Quantum parallelism facilitates rapid morphological analysis and feature extraction，substantially accelerating fundamental image processing operations. Aside from basic processing，quantum-enhanced methods for edge detection，segmentation，and feature matching also demonstrate remarkable efficiency improvements in extracting meaningful information from complex visual data. The security domain benefits substantially from quantum approaches，with innovative encryption protocols based on quantum walks and chaos theory providing theoretically unbreakable protection for sensitive visual data. Quantum watermarking techniques that utilize entanglement phenomena offer fundamentally undetectable data-hiding capabilities，creating new paradigms for digital rights management and secure communication. In machine learning applications，quantum support vector machines enhance classification accuracy for high-dimensional image data，and quantum convolutional neural networks revolutionize feature extraction through inherent quantum parallelism. Quantum generative adversarial networks enable sophisticated synthetic data generation and have notable applications in data augmentation and privacy preservation. This review emphasizes an integrated perspective that connects theoretical advances with practical implementations across multiple application domains. Our structured analytical framework clearly reveals how abstract quantum principles translate into operational capabilities while simultaneously addressing the critical challenges currently hindering widespread adoption. The noisy intermediate-scale quantum era presents substantial hardware limitations，where qubit decoherence and elevated error rates remain persistent barriers to reliable operation. The considerable theory-practice implementation gap，where algorithm performance on actual quantum devices often falls short of theoretical predictions，requires careful consideration. Moreover，scalability issues in real-world applications，where the substantial overhead of quantum data encoding and measurement can diminish computational advantages，present additional hurdles that must be overcome. Resource management considerations examine how different representation models directly affect quantum circuit complexity and execution time，providing crucial insights for practical algorithm design and implementation. Looking forward，several promising pathways can be taken. Examples include hybrid quantum-classical architectures that leverage the complementary strengths of both computational paradigms，advanced error mitigation techniques that compensate for current hardware imperfections，and quantum-inspired classical algorithms that can deliver enhanced performance on conventional hardware while preserving certain quantum advantages. The discussion extends to current hardware platforms，including superconducting qubits，trapped ions，and photonic quantum systems，to provide practical guidance for experimental research and implementation strategies. Through systematic analysis and critical evaluation，this review demonstrates quantum computing’s massive potential to achieve exponential acceleration in computational tasks，enhance security beyond classical limitations，and enable unprecedented capabilities in image analysis and understanding. Emerging applications in medical imaging，remote sensing，and security systems highlight the transformative potential of these technologies. As quantum hardware continues its steady evolution，the strategic integration of quantum approaches into conventional image processing workflows promises to redefine computational boundaries across numerous disciplines，including healthcare，environmental monitoring，industrial inspection，and artificial intelligence. The field currently stands at the threshold of a new computational paradigm，requiring continued technical innovation and strategic vision to successfully navigate the coming transition. By synthesizing recent advances，clearly identifying current limitations，and outlining promising research directions，this survey aims to accelerate the development and adoption of QIP technologies. The ultimate integration of quantum approaches with classical image processing methodologies promises to create powerful hybrid frameworks that leverage the distinctive strengths of both computational paradigms，fundamentally transforming how we extract knowledge and value from visual data in the emerging quantum era and enabling capabilities beyond the reach of either approach independently. The mentioned algorithms are summarized at https://github.com/cococococo7/QIIP.