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The IEEE International Conference on Image Processing (ICIP 2025), “Imaging in the Age of GenAI”, took place from September 14 to 18, 2025, in Anchorage, Alaska, USA. ICIP this year features a robust program of keynote lectures, oral and poster presentations, and workshops covering the latest breakthroughs in signal and image processing. It places a strong focus on machine learning and its subfields as a core driving force in image and video processing. Biomedical imaging continues to be a major area of interest. The conference is interested in traditional medical imaging and medical image analysis, but also in emerging biological image analysis and biomedical signal processing methods. Another major thematic area is computational imaging and related hardware-algorithm co-design. Topics include sparse, low-rank, and low-dimensional modeling; optimization-based inverse problem methods; sensor fusion; novel acquisition modalities. There is also interest in methods that quantify uncertainty and performance in computational imaging. Image and video techniques of representation, processing, analysis, and retrieval are also broadly covered.

MCL members presented four works in ICIP 2025, including “MA-YOLO: Video Object Detection via Motion-assisted YOLO” (by Xinyu Wang), “A Green Learning Approach to LDCT Image Restoration” (by Wei Wang), “MXB: Multi-stage XGboost for Efficient Tree-based Gradient Boosting” (by Haiyi Li), “SHFE: Transparent and Green Image Classification via Supervised Hierarchical Feature Extraction” (by Jie-En Yao). The works we proposed involves the fundamental learning module study as well as approaches following the Green Learning scheme to important applications in image and video processing. The principle of our proposed methods mainly lies in mathematical transparency, competitive performance, and outstanding efficiency in space and time complexity.

In addition to pioneering academic presentations, ICIP also offered professional development and networking opportunities, such as technical tutorials, industry workshops, student [...]

The 2025 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) was held in San Jose from August 6 to 8. The event commenced with three keynote addresses: Prof. Prem Devanbu (University of California, Davis) discussed the reliability of large language models for code generation and offered guidance on when their results can be trusted. Prof. Edward Y. Chang (Stanford University) presented adaptive multi-modal learning as a way to address LLM limitations. Dr. Ed H. Chi (Google DeepMind) spoke on the future of AI-assisted discovery, highlighting systems that enhance rather than replace human expertise.During the conference, Mahtab Movhhedrad, a member of the Media Communications Lab (MCL), presented the paper “GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazing.” This work introduced GUSL-Dehaze, a physics-based green learning framework for image dehazing that completely avoids deep neural networks. The method begins with a modified Dark Channel Prior for initial dehazing, followed by a U-shaped architecture enabling unsupervised representation learning. Feature-engineering techniques such as Relevant Feature Test (RFT) and Least-Squares Normal Transform (LNT) were employed to keep the model compact and interpretable. The final dehazed image is produced through a transparent supervised learning stage, allowing the method to achieve performance comparable to deep learning approaches while maintaining a low parameter count and mathematical transparency.The conference also included a panel session, “Learning Beyond Deep Learning (LB-DL) for Multimedia Processing,” chaired by Prof. Ling Guan (Toronto Metropolitan University/Ryerson University) and Prof. C.-C. Jay Kuo (University of Southern California, Director of the MCL Lab). Prof. Kuo discussed emerging paradigms that challenge the dominance of deep learning, emphasizing the growing importance of interpretability, efficiency, and sustainability in shaping the next generation of multimedia research.

Magnetic Resonance Imaging(MRI) is a non-invasive, radiation-free scanning procedure generally used to obtain images of internal organs. This imaging modality is a popular screening technique for Prostate Cancer. A Prostate MRI may be used to detect prostate cancer, determine the requirement for biopsy, guide needles during targeted MRI biopsy, or detect the spreading of cancer to neighboring areas. Current standards of MRI acquisition still lead to errors, like false detections, where patients are unnecessarily sent to biopsy, or missed detections, where existing tumors remain undetected. A good quality MRI is essential to ensure that Prostate Cancer is diagnosed on time.

An MRI has several sequences, namely T2W, ADC, and DCE. For an MRI to be of diagnostic quality, atleast two of the three sequences must independently be of diagnostic quality. Errors/artifacts may be present in some or all of these sequences. Some common artifacts include motion artifacts, rectal gas, hip prosthesis, etc., that affect the quality of the MRI.

In order to assess the quality of an MRI, we train independent models for each of these sequences. While MRI images are volumetric, we treat each slice of the MRI independently. 2D Haar Wavelet Transform is applied to extract features from the LL, LH, HL, and HH bands. These features are extracted at two different resolutions. The Discriminant Feature Test(DFT) is used to reduce the feature dimension by removing features with a high DFT loss. An XGBoost Classifier is then trained using these selected features to predict whether each slice of the MRI is of satisfactory quality or not. The quality predictions from each of the three sequences are then combined to obtain the final MRI quality prediction. This approach is light-weight, efficient, and explainable, with [...]

Wavelet-based Green Learning (GreenWave) is a new image classification framework that combines the multi-scale power of wavelet transforms with the efficiency and interpretability of Green Learning principles. It avoids backpropagation and replaces traditional deep learning architectures with a transparent, feedforward pipeline.

At its core, GreenWave begins by applying a discrete wavelet transform (DWT)—typically using Haar wavelets—to each image, capturing both local and global spatial structures at multiple resolutions. It extracts features from wavelet subbands (like LL, LH, HL, HH) as well as local image patches from different regions (e.g., east, south, west, north). These features are used to construct class templates via averaging over training examples.

GreenWave operates in three rounds of classification:1. Round-1 classifies easy samples using cosine similarity between the input and class templates (one-vs-rest). It uses confidence metrics (like entropy) to decide which samples are “easy” and can exit the pipeline early.2. Round-2 focuses on semi-hard samples using updated templates and discriminant feature test (DFT) masks to emphasize class-informative coefficients. It also introduces one-vs-one templates to resolve class confusion.3. Round-3 targets the hardest samples using further refined templates and updated confusion masks, maximizing accuracy while maintaining interpretability.Throughout all stages, GreenWave uses cosine similarity as its feature-matching metric and XGBoost classifiers for decision learning, completely bypassing gradient-based training.

Overall, GreenWave demonstrates that a non-backpropagation, wavelet-template-based system can achieve near state-of-the-art performance while being highly efficient, explainable, and modular. This makes it an ideal choice for low-resource or transparent AI applications.