International Journal of Image Processing and Pattern Recognition

International Journal of Image Processing and Pattern Recognition International Journal of Image Processing and Pattern Recognition (IJIPPR) is a peer-reviewed, hybrid open-access journal launched in 2015 that acknowledges papers concerned with image processing and pattern recognition. All topics from image analysis to quadratic discriminant analysis fall under the scope and focus of this journal.

• Image representation and analysis: image transforms and moment-based representations including Legendre, Zernike, and complex moments, multiscale and sparse representations, image sampling and reconstruction, noise sensitivity and information redundancy, and geometric invariance in representation.
• Image enhancement and restoration: denoising and deblurring, contrast and colour correction, artefact removal, inverse problem formulations, and learned restoration priors.
• Image and video compression: transform and predictive coding, lossless and lossy still image compression, colour transformation and quantisation, block- and segment-based video coding, motion estimation and compensation, rate–distortion optimisation, and learned compression methods.
• Feature extraction and representation learning: global and local descriptors, pixel- and region-level feature extraction, biologically inspired hierarchical feature models, colour space selection and filtering, handcrafted versus learned features, and feature selection and fusion.
• Pattern recognition and classification: statistical and structural pattern recognition, discriminant analysis and linear classifiers, clustering and unsupervised methods, kernel and ensemble classifiers, evaluation protocols and benchmarking, and handling of limited and imbalanced training data.
• Artificial intelligence and deep learning in image processing: convolutional and transformer architectures for vision, generative adversarial and diffusion models, transfer and self-supervised learning, deep learning for object recognition and segmentation, and AI-based medical image analysis.
• Image understanding and scene interpretation: object recognition and localisation, semantic scene analysis, domain-specific process modelling for image workflows, knowledge-driven interpretation, and integration of vision with reasoning.
• Three-dimensional imaging and immersive applications: 3D reconstruction from multiple views, depth estimation and stereoscopic vision, point cloud processing, augmented and virtual reality integration, and 3D modelling for medical and industrial imaging.
• Medical and biological image analysis: modality-specific processing, lesion detection and segmentation, registration and quantitative imaging, microscopy and histopathology analysis, and computer-aided diagnosis workflows.
• Big data and cloud-based image analytics: scalable and distributed image processing pipelines, cloud platforms for large-scale image workloads, management and indexing of large image repositories, and distributed pattern recognition architectures.
• Quantum approaches to image processing: quantum representations of image data, quantum algorithms for compression and transformation, quantum pattern recognition methods, and evaluation of quantum image analysis frameworks.
• Image security and forensics: steganography and steganalysis, watermarking and cryptographic image authentication, tamper detection and anti-forgery techniques, manipulated and synthetic image detection, and pattern recognition for security applications.
• Explainable and ethical artificial intelligence: interpretable models for image-based decisions, saliency and attribution methods, bias detection in image datasets and models, fairness and transparency in automated visual analysis, and ethical considerations in surveillance and medical imaging deployment.