Image Processing

Cryo-EM dataset of 80S ribosomes from yeast. This dataset has been described in Dashti et al. (2014, PNAS) "Trajectories of the ribosome as a Brownian nanomachine". In that study, a subset of the dataset was used to demonstrate the performance of a machine learning technique (now termed ManifoldEM) using manifold embedding to determine the energy landscape of a molecule. The dataset is re-analyzed in Seitz et al.

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The results of the experiemental work presented in the journal paper with the title : Reconstruction and Visualization of 5µm Sectional Coronal Views for Macula Vasculature in OCTA, which has the following abstratct

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This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).

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The experimental data in this paper comes from the bamboo sticks provided by farmers who sell bamboo in Anji. We randomly grab less than 100 bamboo sticks and bundle them together. The heights of 5cm, 10cm, 15cm, and 20cm were taken from the front and left and right inclination to take pictures, screen clear and effective experimental data, and then use labelimg software to label them. The sparse bamboo stick samples collected were 600.

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data

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This dataset contains 1050 multi-pattern multi-bin wafer bin maps (WBMs) synthesized from WM-811K binary WBM dataset and real world multi-bin WBMs using a trained pix2pix model.

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A crowdsourcing subjective evaluation of viewport images obtained with several sphere-to-plane projections was conducted. The viewport images were rendered from eight omnidirectional images in equirectangular format. The pairwise comparison (PC) method was chosen for the subjective evaluation of projections. More details about the viewport images and subjective evaluation procedure can be found in [1].

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some forest foggy images

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Supplemental material with appendix, results and color profile data.

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One of the weak points of most of denoising algoritms (deep learning based ones) is the training data. Due to no or very limited amount of groundtruth data available, these algorithms are often evaluated using synthetic noise models such as Additive Zero-Mean Gaussian noise. The downside of this approach is that these simple model do not represent noise present in natural imagery. For evaluation of denoising algorithms’ performance in poor light conditions, we need either representative models or real noisy images paired with those we can consider as groundtruth.

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