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A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library.
Resources for deep learning with satellite & aerial imagery
...Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful please give it a star and consider sponsoring it. You can also follow me on Twitter and LinkedIn where I aim to post frequent updates on my new discoveries, and I have created a dedicated group on LinkedIn. I have also started a blog here and have published a post on the history of this repository called Dissecting the satellite-image-deep-learning repo.
Approximate Adder for FPGA-based Image Processing Applications
Using Approximate Computing, we have designed an innovative approximate adder with real-world image processing applications. The uploaded project includes its Matlab and Verilog models for result reproduction. This is an ongoing study and we aim to formally publish it for better understanding of viewers.
JPhotoShop is based on java. The aim of JPhotoShop is to offer an effetvie image processing system . JPhotoShop is likely to achieve most function of Adobe PhotoShop you can read more about jphotoshop from: http://souljava.blog.163.com
kradview is a viewer of images obtained for some different sources: X-ray, NMR and all DICOM-compatible imaging devices. Its aim is to be a complete platform for medical imaging and image processing.