Publications & Patents

Publications

Peer-reviewed research in computational imaging, medical image analysis, and applied machine learning. Complete list on Google Scholar.

Image Restoration & Computational Imaging

Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring

Malek S, Zhang H, Lee C, Monga V.
European Conference on Computer Vision (ECCV), 2026

To recover images from unknown blur, UPADNet separates Fourier phase and amplitude and learns the updates of an alternating optimization algorithm. It reaches 34.63 dB PSNR on GoPro and shows stronger restoration under noise and limited training data in controlled COCO experiments.

Paper

EUMD: Event-based Unrolled Motion Deblurring via Learnable Prior Optimization

Zhang H, Fu D, Miller J, Choudhury A, Lee C, Monga V.
IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), 2026

EUMD recovers motion-blurred images by combining event-conditioned blur modeling, unrolled half-quadratic splitting, and a Transformer-enhanced U-Net prior. It reaches 37.35 dB PSNR on synthetic GoPro and 32.42 dB on real EVRB, improving on ClearSight and CMTA by 0.31 dB and 1.04 dB, respectively.

Paper

Domain Enriched Learning for Skull Induced Phase Aberration Correction in Ultrasound Neuromodulation

Zhang H, Biswas R, Kiani M, Monga V.
Neuroelectronics, 2025

To correct skull-induced distortion of focused ultrasound, DB-SIPAC combines CT-derived propagation paths with full-skull features and refined time-delay targets. In 2D acoustic simulations, it predicts delays in 0.45 seconds and achieves mean normalized focal pressure of 0.9923.

Paper

Deep, convergent, unrolled half-quadratic splitting for image deconvolution

Zhao Y, Li Y, Zhang H, Monga V, Eldar YC.
IEEE Transactions on Computational Imaging, 2024

DECUN addresses the loss of convergence guarantees in learned iterative restoration. Structured half-quadratic-splitting parameters retain provable convergence and its rate as network depth increases. A 30-layer configuration uses 1,117 trainable parameters and achieves competitive non-blind restoration, including under reduced training data.

Paper

A convergent neural network for non-blind image deblurring

Zhao Y, Li Y, Zhang H, Monga V, Eldar YC.
IEEE International Conference on Image Processing (ICIP), 2023

DCUNBD restores images with known blur kernels through structured, learned half-quadratic splitting. The analysis establishes convergence as depth increases; experiments support the theory and show improved restoration over the compared conventional, convolutional-network, and unrolled baselines.

Paper

TransER: Hybrid Model and Ensemble-Based Sequential Learning for Non-Homogenous Dehazing

Hoang T, Zhang H, Yazdani A, Monga V.
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023

TransER removes spatially varying haze by combining physical scene estimation with a Transformer–convolution model and distilled ensemble reconstruction. Its 2.6M-parameter model reaches PSNR/SSIM of 17.03 dB/0.597 on Dense-Haze and 21.64 dB/0.743 on NH-Haze.

Paper

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

Ancuti CO, Ancuti C, Vasluianu FA, et al.
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023

I participated with team iPAL-LightDehaze using TransER, a lightweight combination of physical haze estimation, Transformer–convolution features, and distilled reconstruction. Our submission ranked 4th in LPIPS among 17 finalists (0.384), with 2.6M parameters and no extra training data.

Paper

Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report

Conde MV, Kolmet M, Seizinger T, et al.
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023

I participated with team IPAL-Bokeh using SGLMS, which combines foreground segmentation, lens-specific mappings, and mask-guided fusion to transform bokeh while preserving the foreground. Our 7M-parameter submission achieved the lowest real-image LPIPS (0.2161) among submitted challenge methods.

Paper

NTIRE 2021 nonhomogeneous dehazing challenge report

Ancuti CO, et al.
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021

I participated with team iPAL-GridFFA using a 3 × 6 grid generator with feature-fusion attention and a patch-based discriminator for non-homogeneous dehazing. Our submission ranked 2nd in SSIM (0.839) among 23 finalists and tied for the lowest reported VGG-based LPIPS (0.194).

Paper

Medical Image Analysis

Hemibrain growth as a biomarker for whole-brain growth

Bajaj US, Yu M, Templeton K, Mukherjee S, Zhang H, Nunn N, Kulkarni AV, Kestle JRW, Monga V, Schiff SJ.
Journal of Neurosurgery: Pediatrics, 2026, pp. 1–11. Advance online publication.

When shunt artifacts obscure brain MRI, this study combines Dense U-Net segmentation with measurement of the unaffected hemisphere. Results from 75 pediatric hydrocephalus patients support hemibrain growth as a proxy for whole-brain growth; brain and cerebrospinal-fluid segmentation achieve Dice scores of 94.7% and 94.2%.

Paper

Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network

Gao Z, Shi J, Zhang X, Li Y, Zhang H, Wu J, et al.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021

CHR-Net addresses crowded nuclei and similar grades in clear-cell renal cell carcinoma using W-Net instance segmentation, high-resolution features, and cross-category classification. The study contains 1,000 patches and 70,945 annotated nuclei; reported performance reaches Dice 0.8790 and average class-wise panoptic quality 0.5458.

Paper

Pimip: An open source platform for pathology information management and integration

Wu J, Mao A, Bao X, Zhang H, Gao Z, Wang C, Gong T, Li C.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021

PIMIP addresses fragmented pathology workflows by integrating whole-slide viewing, collaborative annotation, extensible deep-learning analysis, and patient/report management. The open-source platform demonstrates automatic nucleus labeling, manual correction, tumor analysis, and linked structured information in one web environment.

Paper

A precision diagnostic framework of renal cell carcinoma on whole-slide images using deep learning

Wu J, Zhang R, Gong T, Bao X, Gao Z, Zhang H, Wang C, Li C.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021

This whole-slide renal cancer framework combines tumor detection, subtype classification, ISUP grading, and case-level report generation. Using 667 TCGA slides and 632 hospital slides, its base tumor/non-tumor classifier achieves validation accuracy of 92.2% and 92.7%, respectively; targeted augmentation improves uncertain predictions.

Paper

A personalized diagnostic generation framework based on multi-source heterogeneous data

Wu J, Zhang R, Gong T, Zhang H, Wang C, Li C.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021

To personalize renal cancer prognosis, this framework retrieves histologically similar cases and links images with pathology and clinical reports. Kaplan–Meier and Lasso-Cox analyses generate patient-specific prognostic weights and risk stratification, evaluated using matched data from 859 TCGA patients.

Paper

Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional Network

Wu J, Tang K, Zhang H, et al.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020

An attention-based graph convolutional network combines semantic, syntactic, and sequential text relationships to extract structured information from pathology reports. On 3,632 annotated TCGA reports across four cancer types, it improves macro-F1 on most evaluated tasks, reaching 0.823 for cancer type and 0.871 for histological grade.

Paper

Effects of annotation granularity in deep learning models for histopathological images

Shi J, Gao Z, Zhang H, et al.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019

This study compares bounding-box, ellipse, and pixel-level annotations for histopathology classification and segmentation. Across the tested models, finer annotations generally improve performance; U-Net segmentation reaches 95.79% accuracy with pixel-level labels, versus 89.58% with bounding-box labels.

Paper

OpenHI2—Open source histopathological image platform

Puttapirat P, Zhang H, Deng J, et al.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019

OpenHI2 extends whole-slide annotation with calibrated virtual magnification, shared diagnostic-region selection, inter-rater agreement tests, and modular machine-learning analysis. An annotation trial with two pathologists demonstrates the workflow across 20 diagnostic regions and 1,883 selected clusters.

Paper

OpenHI: Open Platform for Histopathological Image Annotation

Puttapirat P, Zhang H, Deng J, et al.
International Journal of Data Mining and Bioinformatics, 2019

OpenHI combines superpixel-guided region selection, standardized grading labels, responsive region retrieval, and multi-user web annotation for gigapixel slides. Tests demonstrate pixel-level annotation with roughly 300 ms typical response time under the reported viewing conditions, supporting efficient creation of research datasets.

Paper

OpenHI—An open source framework for annotating histopathological image

Puttapirat P, Zhang H, Lian Y, et al.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018

The original OpenHI framework enables collaborative, semantically enriched whole-slide annotation through multi-scale superpixels and a virtual-magnification indicator. Testing on TCGA slides demonstrates responsive pixel-level annotation, providing an open-source foundation for large pathology datasets.

Paper

Applied Machine Learning

BERT-Based Meta-Learning Approach with Looking Back for Sentiment Analysis of Literary Book Reviews

Bao H, He K, Yin X, Li X, Bao X, Zhang H, Wu J, Gao Z.
Natural Language Processing and Chinese Computing (NLPCC), 2021, pp. 235–247

To handle noisy star-derived labels and class imbalance, BERT-MLB learns sample weights from historical features using an LSTM meta-model. On the Chinese literary-review benchmark, macro-F1 rises from 61.12% to 74.63%, a gain of 13.51 percentage points.

Paper

Doctoral Dissertation

Model-Based Deep Learning for Imaging Inverse Problems

Haichuan Zhang · Ph.D. dissertation, Penn State, 2026.

Doctoral research combining physical imaging models, optimization, and deep learning for interpretable image restoration.

Dissertation

Manuscript Under Review

Cascaded, convergent unrolled networks for blind image deconvolution

Malek S, Zhang H, Eldar YC, Monga V.
Under review.

Co-authored research on convergent unrolled networks for blind deconvolution, which jointly estimates a sharp image and its unknown blur kernel.

Patents

Granted Patents

Cloud-based large-scale pathological image collaborative annotation method and system

Li C, Puttapirat P, Zhang H · Xi’an Jiaotong University.

US11392634B2 · Granted Jul 19, 2022.
CN109949907B · Granted Jul 13, 2021.

Patent Applications

Event-based Image and Video Restoration

Co-inventor on U.S. and European patent applications filed by Dolby Laboratories, Inc. Details remain confidential pending publication.

Diffusion-model-based Signal / Data Restoration

Co-inventor on a U.S. patent application filed with NEC Laboratories America, Inc. Details remain confidential pending publication.

Pathological Image Prognostic Feature Weight Calculation Method and System

Li C, Wu J, Gao Z, Zhang H, Puttapirat P · Xi’an Jiaotong University.

CN112434172A · Filed Oct 29, 2020; published Mar 2, 2021.