We propose and demonstrate a holographic imaging scheme exploiting random illuminations for recording hologram and then applying numerical reconstruction and twin image removal. We use an in-line holographic geometry to record the hologram in terms of the second-order correlation and apply the numerical approach to reconstruct the recorded hologram. This strategy helps to reconstruct high-quality quantitative images in comparison to the conventional holography where the hologram is recorded in the intensity rather than the second-order intensity correlation. The twin image issue of the in-line holographic scheme is resolved by an unsupervised deep learning based method using an auto-encoder scheme. Proposed learning technique leverages the main characteristic of autoencoders to perform blind single-shot hologram reconstruction, and this does not require a dataset of samples with available ground truth for training and can reconstruct the hologram solely from the captured sample. Experimental results are presented for two objects, and a comparison of the reconstruction quality is given between the conventional inline holography and the one obtained with the proposed technique.

Phase of an object plays a crucial role in retrieving the complete information. Digital in-line holography is a simple and effective technique to retrieve the phase information and 3D features of object. However the twin image formation limits the total information that can be obtained using in-line holography. Here in this paper, we show how deep learning can be leveraged to reconstruct the inline hologram to remove the twin image and thus preserve the amplitude and phase information of the object. Two different light sources (532nm and 632nm wavelengths) were used to illuminate the object sequentially and the separate holograms were reconstructed with corresponding wavelengths.

The wavefront is scrambled when coherent light propagates through a random scattering medium and which makes direct use of the conventional optical methods ineffective. In this paper, we propose and demonstrate a structured light illumination for imaging through an opaque scattering layer. Proposed technique is reference free and capable to recover the complex field from intensities of the speckle patterns. This is realized by making use of the phase-shifting in the structured light illumination and applying spatial averaging of the speckle pattern in the intensity correlation measurement. An experimental design is presented and simulated results based on the experimental design are shown to demonstrate imaging of different complex-valued objects through scattering layer.

A diffraction-limited condition limits the spatial resolution of the imaging schemes. In this paper, we discuss incoherent illumination and imaging in terms of the second-order correlation to improve the resolution and reconstruction quality. A comparison of performance based on conventional imaging in free space, the average intensity of the speckles with incoherent illumination, and the intensity correlation of the imaged speckles is discussed and examined by simulation tests of these three cases. Simulation results for imaging in two cases viz. conventional imaging and with second-order correlation measurement are presented and discussed. The approach can be used to enhance the quality of reconstruction in quantitative imaging and microscopy.

We tried out various EfficientNet models (B0, B3, B5) as base models utilizing similar data resolution, volume and other hyperparameters, and discovered that larger EfficientNet models did not necessarily lead to better classification performance. We accounted for class imbalance in the data to make our method robust for real- world scenarios. The best result was found to be from lowest complexity model, EfficientNet B0. Our research found that the EfficientNet B0 model demonstrated exceptional performance with a macro average F1 Score of 99.8% and an Accuracy of 99.8%. Additionally, our results also revealed that the EfficientNets B0, B3, B5 models are particularly well-suited for multiclass classification based on highly imbalanced OCT2017 dataset. High classification scores are achieved due to several factors, such as data enhancement, resolution scaling, fine- tuning, and successful transfer learning using ImageNet weights. Based on our preliminary results our approach performs as well or outperforms other known approaches. Our goal is to provide assistance to medical staff in diagnositic process with the help of artificial intelligence (AI) algorithms. Improving the efficiency and accuracy of diagnosis is important in the field of medicine, and the use of AI algorithms such as the one proposed in this research has the potential to make a significant impact in this regard.

In this paper, a novel approach to pupillometry that uses deep-learning (DL) methodologies applied to Visible Light (VL) images is presented. Public iris datasets (e.g., UBIRISv2), as well as data augmentation techniques, were used to train the models to make them robust to noise in the images.

Pupil reflex to variations in illumination and associated dynamics are of importance in neurology and ophthalmology. This is typically measured using a near Infrared (IR) pupillometer to avoid Purkinje reflections that appear when strong Visible Light (VL) illumination is present. Previously we demonstrated the use of deep learning techniques to accurately detect the pupil pixels (segmentation binary mask) in case of VL images for performing VL pupillometry. Here, we present a method to obtain the parameters of the elliptical pupil boundary along with the segmentation binary pupil mask. This eliminates the need for an additional, computationally expensive post-processing step of ellipse fitting and also improves segmentation accuracy. Using the time-varying ellipse parameters of pupil, we can compute the dynamics of the Pupillary Light Reflex (PLR). We also present preliminary evaluations of our deep learning algorithms on experimental data. This work is a significant push in our goal to develop and validate a VL pupillometer based on a smartphone that can be used in the field.