Pmapper is a super-resolution and deconvolution toolkit for python 3.6+

Related tags

Deep Learningpmapper
Overview

pmapper

pmapper is a super-resolution and deconvolution toolkit for python 3.6+. PMAP stands for Poisson Maximum A-Posteriori, a highly flexible and adaptable algorithm for these problems. An implementation of the contemporary Richardson-Lucy algorithm is included for comparison.

The name of this repository is an homage to MTF-Mapper, a slanted edge MTF measurement program written by Frans van den Bergh.

The implementations of all algorithms in this repository are CPU/GPU agnostic and performant, able to perform 4K restoration at hundreds of iterations per second.

Usage

Basic PMAP, Multi-frame PMAP

import pmapper

img = ... # load an image somehow
psf = ... # acquire the PSF associated with the img
pmp = pmapper.PMAP(img, psf)  # "PMAP problem"
while pmp.iter < 100:  # number of iterations
    fHat = pmp.step()  # fHat is the object estimate

In simulation studies, the true object can be compared to fHat (for example, mean square error) to track convergence. If the psf is "larger" than the image, for example a 1024x1024 image and a 2048x2048 psf, the output will be super-resolved at the 2048x2048 resolution.

PMAP is able to combine multiple images of the same objec with different PSFs into one with the multi-frame variant. This can be used to combat dynamical atmospheric seeing conditions, line of sight jitter, or even perform incoherent aperture synthesis; rendering images from sparse aperture systems that mimic or exceed a system with a fully filled aperture.

import pmapper

# load a sequence of images; could be any iterable,
# or e.g. a kxmxn ndarray, with k = num frames
# psfs must have the same "size" (k) and correspond
# to the images in the same indices
imgs = ...
psfs = ...
pmp = pmapper.MFPMAP(imgs, psfs)  # "PMAP problem"
while pmp.iter < len(imgs)*100:  # number of iterations
    fHat = pmp.step()  # fHat is the object estimate

Multi-frame PMAP cycles through the images and PSFs, so the total number of iterations "should" be an integer multiple of the number of source images. In this way, each image is "visited" an equal number of times.

GPU computing

As mentioned previously, pmapper can be used trivially on a GPU. To do so, simply execute the following modification:

import pmapper
from pmapper import backend

import cupy as cp
from cupyx.scipy import (
    ndimage as cpndimage,
    fft as cpfft
)

backend.np._srcmodule = cp
backend.fft.fft = cpfft
backend.ndimage._srcmodule = cpndimage

# if your data is not on the GPU already
img = cp.array(img)
psf = cp.array(psf)

# ... do PMAP, it will run on a GPU without changing anything about your code

fHatCPU = fHat.get()

cupy is not the only way to do so; anything that quacks like numpy, scipy fft, and scipy ndimage can be used to substitute the backend. This can be done dynamically and at runtime. You likely will want to cast your imagery from fp64 to fp32 for performance scaling on the GPU.

Owner
NASA Jet Propulsion Laboratory
A world leader in the robotic exploration of space
NASA Jet Propulsion Laboratory
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

Duo Li 273 Dec 18, 2022
YoloV3 Implemented in Tensorflow 2.0

YoloV3 Implemented in TensorFlow 2.0 This repo provides a clean implementation of YoloV3 in TensorFlow 2.0 using all the best practices. Key Features

Zihao Zhang 2.5k Dec 26, 2022
An exploration of log domain "alternative floating point" for hardware ML/AI accelerators.

This repository contains the SystemVerilog RTL, C++, HLS (Intel FPGA OpenCL to wrap RTL code) and Python needed to reproduce the numerical results in

Facebook Research 373 Dec 31, 2022
Model Zoo of BDD100K Dataset

Model Zoo of BDD100K Dataset

ETH VIS Group 200 Dec 27, 2022
Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification

S-multi-SNE Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification A repository containing the code to reproduce the findings

Theodoulos Rodosthenous 3 Apr 15, 2022
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.

ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.

Snapdragon Lee 2 Dec 16, 2022
Tooling for the Common Objects In 3D dataset.

CO3D: Common Objects In 3D This repository contains a set of tools for working with the Common Objects in 3D (CO3D) dataset. Download the dataset The

Facebook Research 724 Jan 06, 2023
An Open-Source Toolkit for Prompt-Learning.

An Open-Source Framework for Prompt-learning. Overview • Installation • How To Use • Docs • Paper • Citation • What's New? Nov 2021: Now we have relea

THUNLP 2.3k Jan 07, 2023
Code accompanying the paper on "An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers" published at NeurIPS, 2021

Code for "An Empirical Investigation of Domian Generalization with Empirical Risk Minimizers" (NeurIPS 2021) Motivation and Introduction Domain Genera

Meta Research 15 Dec 27, 2022
Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

this is a simple artificial neural network model using deep learning and torch-audio to classify cats and dog sounds.

crispengari 3 Dec 05, 2022
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Python Streaming Anomaly Detection (PySAD) PySAD is an open-source python framework for anomaly detection on streaming multivariate data. Documentatio

Selim Firat Yilmaz 181 Dec 18, 2022
A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

CoVA: Context-aware Visual Attention for Webpage Information Extraction Abstract Webpage information extraction (WIE) is an important step to create k

Keval Morabia 41 Jan 01, 2023
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (

Jaemin Cho 98 Dec 15, 2022
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package

trRosetta - Pytorch (wip) Implementation of trRosetta and trDesign for Pytorch, made into a convenient package

Phil Wang 67 Dec 17, 2022
Implement face detection, and age and gender classification, and emotion classification.

YOLO Keras Face Detection Implement Face detection, and Age and Gender Classification, and Emotion Classification. (image from wider face dataset) Ove

Chloe 10 Nov 14, 2022
FANet - Real-time Semantic Segmentation with Fast Attention

FANet Real-time Semantic Segmentation with Fast Attention Ping Hu, Federico Perazzi, Fabian Caba Heilbron, Oliver Wang, Zhe Lin, Kate Saenko , Stan Sc

Ping Hu 42 Nov 30, 2022
Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

mmc installation git clone https://github.com/dmarx/Multi-Modal-Comparators cd 'Multi-Modal-Comparators' pip install poetry poetry build pip install d

David Marx 37 Nov 25, 2022
A library that can print Python objects in human readable format

objprint A library that can print Python objects in human readable format Install pip install objprint Usage op Use op() (or objprint()) to print obj

319 Dec 25, 2022
An off-line judger supporting distributed problem repositories

Thaw 中文 | English Thaw is an off-line judger supporting distributed problem repositories. Everyone can use Thaw release problems with license on GitHu

countercurrent_time 2 Jan 09, 2022
PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Unofficial PyTorch implementation of "Zero-Shot" Super-Resolution using Deep Internal Learning Unofficial Implementation of 1712.06087 "Zero-Shot" Sup

Jacob Gildenblat 196 Nov 27, 2022