Fundus cameras are widely used by ophthalmologists for monitoring and diagnosing retinal pathologies. Unfortunately, no optical system is perfect, and the visibility of retinal images can be greatly degraded due to the presence of problematic illumination, intraocular scattering, or blurriness caused by sudden movements. To improve image quality, different retinal image restoration/enhancement techniques have been developed, which play an important role in improving the performance of various clinical and computer-assisted applications. This paper gives a comprehensive review of these restoration/enhancement techniques, discusses their underlying mathematical models, and shows how they may be effectively applied in real-life practice to increase the visual quality of retinal images for potential clinical applications including diagnosis and retinal structure recognition. All three main topics of retinal image restoration/enhancement techniques, i.e., illumination correction, dehazing, and deblurring, are addressed. Finally, some considerations about challenges and the future scope of retinal image restoration/enhancement techniques will be discussed.

Background: Blurry images in teledermatology and consultation increased the diagnostic difficulty for both deep learning models and physicians. We aim to determine the extent of restoration in diagnostic accuracy after blurry images are deblurred by deep learning models. Methods: We used 19,191 skin images from a public skin image dataset that includes 23 skin disease categories, 54 skin images from a public dataset of blurry skin images, and 53 blurry dermatology consultation photos in a medical center to compare the diagnosis accuracy of trained diagnostic deep learning models and subjective sharpness between blurry and deblurred images. We evaluated five different deblurring models, including models for motion blur, Gaussian blur, Bokeh blur, mixed slight blur, and mixed strong blur. Main Outcomes and Measures: Diagnostic accuracy was measured as sensitivity and precision of correct model prediction of the skin disease category. Sharpness rating was performed by board-certified dermatologists on a 4-point scale, with 4 being the highest image clarity. Results: The sensitivity of diagnostic models dropped 0.15 and 0.22 on slightly and strongly blurred images, respectively, and deblurring models restored 0.14 and 0.17 for each group. The sharpness ratings perceived by dermatologists improved from 1.87 to 2.51 after deblurring. Activation maps showed the focus of diagnostic models was compromised by the blurriness but was restored after deblurring. Conclusions: Deep learning models can restore the diagnostic accuracy of diagnostic models for blurry images and increase image sharpness perceived by dermatologists. The model can be incorporated into teledermatology to help the diagnosis of blurry images.

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings\' energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants\' privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants\' privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants\' visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants\' visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system.

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method\'s ability to recover artificial \'pseudokernels\'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution.Approach. Blind deconvolution is a method of estimating the hypothetical \'deblurred\' image along with the blur kernel (related to the point spread function) simultaneously. Traditionalmaximum a posterioriblind deconvolution methods require stringent assumptions and suffer from convergence to a trivial solution. A method of modelling the deblurred image and kernel with independent neural networks, called \'neural blind deconvolution\' had demonstrated success for deblurring 2D natural images in 2020. In this work, we adapt neural blind deconvolution to deblur PSMA PET images while simultaneous supersampling to double the original resolution. We compare this methodology with several interpolation methods in terms of resultant blind image quality metrics and test the model\'s ability to predict accurate kernels by re-running the model after applying artificial \'pseudokernels\' to deblurred images. The methodology was tested on a retrospective set of 30 prostate patients as well as phantom images containing spherical lesions of various volumes.Main results. Neural blind deconvolution led to improvements in image quality over other interpolation methods in terms of blind image quality metrics, recovery coefficients, and visual assessment. Predicted kernels were similar between patients, and the model accurately predicted several artificially-applied pseudokernels. Localization of activity in phantom spheres was improved after deblurring, allowing small lesions to be more accurately defined.Significance. The intrinsically low spatial resolution of PSMA PET leads to partial volume effects (PVEs) which negatively impact uptake quantification in small regions. The proposed method can be used to mitigate this issue, and can be straightforwardly adapted for other imaging modalities.

OBJECTIVE: Robust implementation of spiral imaging requires efficient deblurring. A deblurring method was previously proposed to separate and deblur water and fat simultaneously, based on image-space kernel operations. The goal of this work is to improve the performance of the previous deblurring method using kernels with better properties.
METHODS: Four types of kernels were formed using different models for the region outside the collected k-space as well as low-pass preconditioning (LP). The performances of the kernels were tested and compared with both phantom and volunteer data. Data were also synthesized to evaluate the SNR.
RESULTS: The proposed \"square\" kernels are much more compact than the previously used circular kernels. Square kernels have better properties in terms of normalized RMS error, structural similarity index measure, and SNR. The square kernels created by LP demonstrated the best performance of artifact mitigation on phantom data.
CONCLUSIONS: The sizes of the blurring kernels and thus the computational cost can be reduced by the proposed square kernels instead of the previous circular ones. Using LP may further enhance the performance.

Magnetic particle imaging (MPI) is an emerging medical imaging technique that has high sensitivity, contrast, and excellent depth penetration. In MPI, x-space is a reconstruction method that transforms the measured voltages into particle concentrations. The reconstructed native image can be modeled as a convolution of the magnetic particle concentration with a point-spread function (PSF). The PSF is one of the important parameters in deconvolution. However, accurately measuring or modeling the PSF in the hardware used for deconvolution is challenging due to the various environment and magnetic particle relaxation. The inaccurate PSF estimation may lead to the loss of the content structure of the MPI image, especially in low gradient fields. In this study, we developed a Dual Adversarial Network (DAN) with patch-wise contrastive constraint to deblur the MPI image. This method can overcome the limitations of unpaired data in data acquisition scenarios and remove the blur around the boundary more effectively than the common deconvolution method. We evaluated the performance of the proposed DAN model on simulated and real data. Experimental results confirmed that our model performs favorably against the deconvolution method that is mainly used for deblurring the MPI image and other GAN-based deep learning models.

BACKGROUND: Efficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks.
RESULTS: In this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmentation in motion blurred images. For this purpose, we first collected a new dataset of matching sharp and naturally blurred image pairs of real sorghum and weed plants from drone images of the same agricultural field. The data was used to train and evaluate the performance of DeBlurWeedSeg on both sharp and blurred images of a hold-out test-set. We show that DeBlurWeedSeg outperforms a standard segmentation model that does not include an integrated deblurring step, with a relative improvement of [Formula: see text] in terms of the Sørensen-Dice coefficient.
CONCLUSIONS: Our combined deblurring and segmentation model DeBlurWeedSeg is able to accurately segment weeds from sorghum and background, in both sharp as well as motion blurred drone captures. This has high practical implications, as lower error rates in weed and crop segmentation could lead to better weed control, e.g. when using robots for mechanical weed removal.

OBJECTIVE: Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k-space truncation, but this does not properly model in-plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k-space regions. To fill this gap, we developed a T2 -deblurred deep learning SR method for the SR of 3D-TSE images.
METHODS: A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high-resolution k-space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase-encoding directions) of genetically engineered mouse embryo model TSE-MR images.
RESULTS: The proposed method can produce high-quality 3 × 3 SR images for a typical 500-slice volume with 6-7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging-quality expert scores for prospective evaluation.
CONCLUSIONS: The proposed T2 -deblurring method improved accuracy and image quality of deep learning-based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.

To present the validation of a new Flexible Ultra-Short Echo time (FUSE) pulse sequence using a short-T2 phantom.
FUSE was developed to include a range of RF excitation pulses, trajectories, dimensionalities, and long-T2 suppression techniques, enabling real-time interchangeability of acquisition parameters. Additionally, we developed an improved 3D deblurring algorithm to correct for off-resonance artifacts. Several experiments were conducted to validate the efficacy of FUSE, by comparing different approaches for off-resonance artifact correction, variations in RF pulse and trajectory combinations, and long-T2 suppression techniques. All scans were performed on a 3 T system using an in-house short-T2 phantom. The evaluation of results included qualitative comparisons and quantitative assessments of the SNR and contrast-to-noise ratio.
Using the capabilities of FUSE, we demonstrated that we could combine a shorter readout duration with our improved deblurring algorithm to effectively reduce off-resonance artifacts. Among the different RF and trajectory combinations, the spiral trajectory with the regular half-inc pulse achieves the highest SNRs. The dual-echo subtraction technique delivers better short-T2 contrast and superior suppression of water and agar signals, whereas the off-resonance saturation method successfully suppresses water and lipid signals simultaneously.
In this work, we have validated the use of our new FUSE sequence using a short T2 phantom, demonstrating that multiple UTE acquisitions can be achieved within a single sequence. This new sequence may be useful for acquiring improved UTE images and the development of UTE imaging protocols.