Computational Imaging

Abstract submission deadline

3 December 2027

Manuscript submission deadline

3 February 2028

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Topic Information

Dear Colleagues,

Computational imaging (CI) is transforming the way information is acquired, reconstructed and interpreted across optical, electromagnetic, biomedical, remote sensing and intelligent sensing systems. By jointly designing sensing hardware, signal models and computational algorithms, it enables high-resolution, high-speed and information-rich imaging beyond the limits of conventional acquisition systems. This Topic invites original research and review articles on advances in CI theory, systems and applications, including radar, millimeter-wave and terahertz imaging, sparse and compressive imaging, metasurface- and antenna-based imaging, image reconstruction, optimization, machine learning and artificial intelligence-enabled sensing. Contributions addressing photonic and optical imaging, sensor arrays, wireless sensing, remote sensing-based image formation, biomedical and cellular imaging and embedded electronic imaging platforms are also welcome. We particularly encourage interdisciplinary studies that combine innovative hardware with robust algorithms to improve resolution, efficiency, interpretability and deployment in real-world environments.

This topic aims to provide a forum for researchers working at the intersection of imaging science, signal processing, photonics, sensing, and computational intelligence.

Dr. Amir Masoud Molaei
Dr. Mengran Zhao
Dr. Shaoqing Hu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Cells cells	6.0	11.4	2012	14.9 Days	CHF 2700	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.8	7.3	2015	21.3 Days	CHF 1800	Submit
Optics optics	1.8	2.6	2020	19.6 Days	CHF 1400	Submit
Photonics photonics	2.1	3.9	2014	13.9 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit

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Published Papers (4 papers)

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All Cells J. Imaging Optics Photonics Sensors Remote Sensing Electronics

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23 pages, 5388 KB  

Open AccessArticle

Self-Supervised OCT Representation Learning with Local Dimensionality Regularization for Automated Retinal Disease Diagnosis

by Xiangge Sun, Wenrui Lin, Chenao Yuan, Jun Xu and Yuemei Luo

Sensors 2026, 26(17), 5338; https://doi.org/10.3390/s26175338 - 23 Aug 2026

Viewed by 245

Abstract

Optical coherence tomography (OCT) is a high-resolution and non-contact optical imaging and sensing modality that provides depth-resolved cross-sectional visualization of retinal microstructures. It plays an important role in the assessment of retinal diseases, including age-related macular degeneration (AMD) and diabetic macular edema (DME). [...] Read more.

Optical coherence tomography (OCT) is a high-resolution and non-contact optical imaging and sensing modality that provides depth-resolved cross-sectional visualization of retinal microstructures. It plays an important role in the assessment of retinal diseases, including age-related macular degeneration (AMD) and diabetic macular edema (DME). However, automated OCT image classification commonly relies on fully supervised models that require large-scale expert annotations, which are costly and time-consuming because of the complex layered anatomy and subtle pathological patterns present in retinal OCT images. To reduce annotation dependence, this study proposes a self-supervised representation learning framework with local dimensionality regularization for retinal OCT image classification. The proposed method estimates the local intrinsic dimensionality of learned representations and incorporates it into an asymptotic Fisher-Rao regularization objective to mitigate local dimensional degeneration and preserve fine-grained structural information. Logarithmic scaling and geometric averaging are further introduced to reduce sensitivity to outliers and improve optimization stability. Experiments on three independent OCT datasets achieved classification accuracies of 94.35%, 92.48%, and 92.56%, respectively, demonstrating competitive performance compared with mainstream self-supervised methods. These results demonstrate that explicitly modeling local feature geometry can improve the discrimination of sensor-acquired OCT images while reducing reliance on manual annotations, providing an effective approach for intelligent analysis of biomedical optical imaging data. Full article

(This article belongs to the Topic Computational Imaging)

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28 pages, 102280 KB  

Open AccessArticle

PRMEFNet: A Real-Time Unsupervised Multi-Exposure Fusion Network Driven by Prior Knowledge

by Junwei Qi, Hangdong Wang, Xu Xiao and Jingpeng Gao

J. Imaging 2026, 12(8), 389; https://doi.org/10.3390/jimaging12080389 - 19 Aug 2026

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Abstract

Due to the limited dynamic range of imaging sensors, most cameras can only capture low-dynamic-range (LDR) images. Multi-exposure fusion (MEF) is an effective technique for generating high-dynamic-range (HDR) images. However, to simultaneously preserve texture details and global exposure, most existing methods primarily rely [...] Read more.

Due to the limited dynamic range of imaging sensors, most cameras can only capture low-dynamic-range (LDR) images. Multi-exposure fusion (MEF) is an effective technique for generating high-dynamic-range (HDR) images. However, to simultaneously preserve texture details and global exposure, most existing methods primarily rely on more complex models to improve performance, resulting in higher computational costs and longer processing times. To address this issue, we propose a real-time unsupervised MEF network driven by prior knowledge. To this end, a hierarchical feature extraction module is designed that utilizes filtering operations to decompose the source images into base layers and detail layers. Features are extracted from each layer separately to reduce the difficulty of extracting effective features. Then, the receptive field of feature maps is expanded by dilated convolutions, and a window-based self-attention mechanism is applied to perform context modeling, achieving effective contextual modeling with low computational cost. Subsequently, texture features and global features are extracted separately to enable the model to maintain both local texture clarity and global smoothness. In addition, a one-dimensional lookup table is utilized to accelerate the inference process. Comprehensive experiments are conducted to verify the effectiveness of the proposed method. The subjective evaluation results demonstrate that the fused images exhibit superior visual quality, while objective experiments further quantify its superior performance, demonstrating that the proposed method effectively reduces computation time. Full article

(This article belongs to the Topic Computational Imaging)

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31 pages, 50087 KB  

Open AccessArticle

Relative-Height Image Generation from Long-Range Airborne Streak-Tube Imaging LiDAR for Wide-Area Building- Structure Mapping

by Chaowei Dong, Zhaodong Chen, Rongwei Fan, Zhiwei Dong, Deying Chen, Pengfei Hao and Lansong Cao

J. Imaging 2026, 12(8), 355; https://doi.org/10.3390/jimaging12080355 - 4 Aug 2026

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Abstract

Wide-area building-structure mapping from long-range airborne LiDAR requires image products that can represent building footprints, roof-height variations, and structural discontinuities with low computational latency. Airborne streak-tube imaging LiDAR (ASTIL) records a spatial–temporal echo image for each laser pulse, where the detector row corresponds [...] Read more.

Wide-area building-structure mapping from long-range airborne LiDAR requires image products that can represent building footprints, roof-height variations, and structural discontinuities with low computational latency. Airborne streak-tube imaging LiDAR (ASTIL) records a spatial–temporal echo image for each laser pulse, where the detector row corresponds to the fan-beam spatial angle, the detector column encodes echo arrival time, and the frame sequence represents the scanning process. This row–column–frame topology makes it possible to generate image-domain structural products directly from raw streak-tube echo sequences. In this paper, a relative-height image generation method is proposed for long-range ASTIL. The method constructs slant-range matrices from raw echo images, suppresses row-wise ground-related range trends, maps the residuals into relative-height values, and generates scan-geometry-calibrated swath-level relative-height images using lightweight calibration rather than rigorous point-wise POS/IMU trajectory reconstruction. Airborne experiments at 2 km, 3 km, and 6 km flight heights show that the proposed workflow can generate relative-height images with spatial sampling intervals of 0.30 m, 0.45 m, and 0.90 m, respectively, within a 0.5 s acquisition window. The generated cropped image products occupy less than 0.3% of the raw streak-image sequence volume, reflecting a compact image-domain representation for rapid preliminary mapping rather than lossless data compression. Building-scale comparisons with UAV LiDAR reference data indicate that the generated images preserve the main building footprints, boundary orientations, and roof-height discontinuities. For nine flat-roof targets, the mean absolute roof-to-ground height errors range from 0.24 m to 0.30 m across the three flight heights. These results suggest that ASTIL relative-height imaging can provide an efficient image-domain representation for wide-area building-structure mapping under long-range airborne observation conditions. Full article

(This article belongs to the Topic Computational Imaging)

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16 pages, 2111 KB  

Open AccessEditor’s ChoiceArticle

Ambiguity-Reduced Depth from Defocus via Single-Shot Polarizer-Free Dual-Focus Imaging

by Wenjie Lai, Fanyu Zeng, Xiao Hu, Shaowei He, Ziji Liu, Huiling Tai and Yadong Jiang

Photonics 2026, 13(8), 732; https://doi.org/10.3390/photonics13080732 - 31 Jul 2026

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Abstract

Single-image depth from defocus is limited by blur-radius ambiguity: two object distances on different sides of the focal plane can produce similar point spread functions (PSFs). We study single-shot dual-focus imaging (DFI) with a polarizer-free liquid crystal (LC) lens, where the ordinary-ray component [...] Read more.

Single-image depth from defocus is limited by blur-radius ambiguity: two object distances on different sides of the focal plane can produce similar point spread functions (PSFs). We study single-shot dual-focus imaging (DFI) with a polarizer-free liquid crystal (LC) lens, where the ordinary-ray component remains unmodulated while the extraordinary-ray component is refocused. Unlike conventional multi-capture DFD, the proposed system records a dual-focus superposition in one exposure and selects the optical setting before network training using the ambiguity-interval length A and PSF correlation $C_r$. We show that DFI does not reduce ambiguity unconditionally: its benefit depends on the LC-lens power. A deblurring-based depth estimation network with a physics-calibrated Wiener bank translates the selected dual-focus cue into quantitative depth. On model-matched synthetic DFI data with an 8 m focus setting, the proposed configuration reduces RMS error from $0.227\pm 0.001$ m to $0.190\pm 0.001$ m (mean ± s.d. over four independent runs) relative to single-focus imaging. A 116-pair indoor prototype dataset provides feasibility evidence under the tested configuration, but is not used to claim broad physical generalization. Full article

(This article belongs to the Topic Computational Imaging)

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