#Super-Resolution

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Fixed Energy

Like filming the bustle of a city at night, life inside a cell can look like chaos. A static snapshot allows us to make sense of the surroundings, to spot subtle details, and perhaps some order in the noise. In these mammalian cells, we see clusters of mitochondria, the tiny organelles that churn out cellular energy, a molecule called ATP. Among other jobs, mitochondria also heat our cells from the inside – each is around a billion times smaller than a nuclear power facility. While watching organelles in motion has its advantages, here a new fluorescent stain reveals fine details in fixed cells – those frozen in time with chemicals – potentially yielding different perspectives on these tiny power stations in health and disease.

Written by John Ankers

Image from work by Jingting Chen and colleagues

College of Future Technology, Institute of Molecular Medicine, National Biomedical Imaging Center, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, China

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in Proceedings of the National Academy of Science (PNAS), April 2024

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The Power of Context: How Multimodality Improves Image Super-Resolution

📄 [PDF 다운로드] 📄 PDF 본문 내용 (영어) The Power of Context: How Multimodality Improves Image Super-Resolution Kangfu Mei* 1,2, Hossein Talebi1, Mojtaba Ardakani1, Vishal M. Patel2, Peyman Milanfar1, Mauricio Delbracio1 1 Google, 2 Johns Hopkins University Project Page: https://mmsr.kfmei.com/ Inputs Outputs Reference A close-up of a male lion with a dark mane, light tan face, and pink tongue sticking…

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The World of Pixel Recurrent Neural Networks (PixelRNNs)

Pixel Recurrent Neural Networks (PixelRNNs) have emerged as a groundbreaking approach in the field of image generation and processing. These sophisticated neural network architectures are reshaping how machines understand and generate visual content. This article delves into the core aspects of PixelRNNs, exploring their purpose, architecture, variants, and the challenges they face.

Purpose and Application

PixelRNNs are primarily engineered for image generation and completion tasks. Their prowess lies in understanding and generating pixel-level patterns. This makes them exceptionally suitable for tasks like image inpainting, where they fill in missing parts of an image, and super-resolution, which involves enhancing the quality of images. Moreover, PixelRNNs are capable of generating entirely new images based on learned patterns, showcasing their versatility in the realm of image synthesis.

Architecture

The architecture of PixelRNNs is built upon the principles of recurrent neural networks (RNNs), renowned for their ability to handle sequential data. In PixelRNNs, the sequence is the pixels of an image, processed in an orderly fashion, typically row-wise or diagonally. This sequential processing allows PixelRNNs to capture the intricate dependencies between pixels, which is crucial for generating coherent and visually appealing images.

Pixel-by-Pixel Generation

At the heart of PixelRNNs lies the concept of generating pixels one at a time, following a specified order. Each prediction of a new pixel is informed by the pixels generated previously, allowing the network to construct an image in a step-by-step manner. This pixel-by-pixel approach is fundamental to the network's ability to produce detailed and accurate images.

Two Variants

PixelRNNs come in two main variants: Row LSTM and Diagonal BiLSTM. The Row LSTM variant processes the image row by row, making it efficient for certain types of image patterns. In contrast, the Diagonal BiLSTM processes the image diagonally, offering a different perspective in understanding and generating image data. The choice between these two depends largely on the specific requirements of the task at hand.

Conditional Generation

A remarkable feature of PixelRNNs is their ability to be conditioned on additional information, such as class labels or parts of images. This conditioning enables the network to direct the image generation process more precisely, which is particularly beneficial for tasks like targeted image editing or generating images that need to meet specific criteria.

Training and Data Requirements

As with other neural networks, PixelRNNs require a significant volume of training data to learn effectively. They are trained on large datasets of images, where they learn to model the distribution of pixel values. This extensive training is necessary for the networks to capture the diverse range of patterns and nuances present in visual data.

Challenges and Limitations

Despite their capabilities, PixelRNNs face certain challenges and limitations. They are computationally intensive due to their sequential processing nature, which can be a bottleneck in applications requiring high-speed image generation. Additionally, they tend to struggle with generating high-resolution images, as the complexity increases exponentially with the number of pixels. Creating a PixelRNN for image generation involves several steps, including setting up the neural network architecture and training it on a dataset of images. Here's an example in Python using TensorFlow and Keras, two popular libraries for building and training neural networks. This example will focus on a simple PixelRNN structure using LSTM (Long Short-Term Memory) units, a common choice for RNNs. The code will outline the basic structure, but please note that for a complete and functional PixelRNN, additional components and fine-tuning are necessary.

PixRNN using TensorFlow

First, ensure you have TensorFlow installed: pip install tensorflow Now, let's proceed with the Python code: import tensorflow as tf from tensorflow.keras import layers def build_pixel_rnn(image_height, image_width, image_channels): # Define the input shape input_shape = (image_height, image_width, image_channels) # Create a Sequential model model = tf.keras.Sequential() # Adding LSTM layers - assuming image_height is the sequence length # and image_width * image_channels is the feature size per step model.add(layers.LSTM(256, return_sequences=True, input_shape=input_shape)) model.add(layers.LSTM(256, return_sequences=True)) # PixelRNNs usually have more complex structures, but this is a basic example # Output layer - predicting the pixel values model.add(layers.TimeDistributed(layers.Dense(image_channels, activation='softmax'))) return model # Example parameters for a grayscale image (height, width, channels) image_height = 64 image_width = 64 image_channels = 1 # For grayscale, this would be 1; for RGB images, it would be 3 # Build the model pixel_rnn = build_pixel_rnn(image_height, image_width, image_channels) # Compile the model pixel_rnn.compile(optimizer='adam', loss='categorical_crossentropy') # Summary of the model pixel_rnn.summary() This code sets up a basic PixelRNN model with two LSTM layers. The model's output is a sequence of pixel values for each step in the sequence. Remember, this example is quite simplified. In practice, PixelRNNs are more complex and may involve techniques such as masking to handle different parts of the image generation process. Training this model requires a dataset of images, which should be preprocessed to match the input shape expected by the network. The training process involves feeding the images to the network and optimizing the weights using a loss function (in this case, categorical crossentropy) and an optimizer (Adam). For real-world applications, you would need to expand this structure significantly, adjust hyperparameters, and possibly integrate additional features like convolutional layers or different RNN structures, depending on the specific requirements of your task.

Recent Developments

Over time, the field of PixelRNNs has seen significant advancements. Newer architectures, such as PixelCNNs, have been developed, offering improvements in computational efficiency and the quality of generated images. These developments are indicative of the ongoing evolution in the field, as researchers and practitioners continue to push the boundaries of what is possible with PixelRNNs. Pixel Recurrent Neural Networks represent a fascinating intersection of artificial intelligence and image processing. Their ability to generate and complete images with remarkable accuracy opens up a plethora of possibilities in areas ranging from digital art to practical applications like medical imaging. As this technology continues to evolve, we can expect to see even more innovative uses and enhancements in the future.

🗒️ Sources

- dl.acm.org - Pixel recurrent neural networks - ACM Digital Library - arxiv.org - Pixel Recurrent Neural Networks - researchgate.net - Pixel Recurrent Neural Networks - opg.optica.org - Single-pixel imaging using a recurrent neural network - codingninjas.com - Pixel RNN - journals.plos.org - Recurrent neural networks can explain flexible trading of… Read the full article

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POLCAM Action!

POLCAM, a modification of a method for super-resolution microscopy called SMOLM by detecting polarisation with the addition of a polarising camera to any wide-field fluorescence microscope, plus open-source analysis software

Read the original research article here

Image from work by Ezra Bruggeman and colleagues

Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in bioRxiv, May 2024 (not peer reviewed)

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The Power of Context: How Multimodality Improves Image Super-Resolution

📄 [PDF 다운로드] 📄 PDF 본문 내용 (영어) The Power of Context: How Multimodality Improves Image Super-Resolution Kangfu Mei* 1,2, Hossein Talebi1, Mojtaba Ardakani1, Vishal M. Patel2, Peyman Milanfar1, Mauricio Delbracio1 1 Google, 2 Johns Hopkins University Project Page: https://mmsr.kfmei.com/ Inputs Outputs Reference A close-up of a male lion with a dark mane, light tan face, and pink tongue sticking…

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