ImageJ Alternatives

Photopea

Online photo editor, which can work with PSD, XCF and Sketch files (Photoshop, Gimp and Sketch App). Convert between PSD, XCF, JPG, PNG and many other formats. .

Features:

• Web-Based
• Open PSD files
• Vectorize raster images
• Sketch to PSD
• Browser based
• Import from Sketch
• Support for Layers
• Online Photo Editor
• Image Editing
• Realtime magic wand tool
• User interface
• Color blending
• Drawing
• Editing photos
• Free API
• Image Editing
• Kids Mode
• Layer manipulation
• Photographic filters
• Convert Photo to Painting
• Photoshop-like
• REST API
• Texture painting
• Web based platform

Fiji

Fiji Is Just ImageJ Fiji is an image processing package. It can be described as a distribution of ImageJ together with Java, Java 3D and a lot of plugins organized into a coherent menu structure. Fiji compares to ImageJ as Ubuntu compares to Linux. The main focus of Fiji is to assist research in life sciences.

Aeskulap

Aeskulap is a medical image viewer. It is able to load a series of special images stored in the DICOM format for review. Additionally Aeskulap is able to query and fetch DICOM images from archive nodes (also called PACS) over the network. The goal of this project is to create a full open source replacement for commercially available DICOM viewers. Aeskulap is based on gtkmm, glademm and gconfmm and designed to run under Linux. Ports of these packages are available for different platforms.

Image Enhancer

Image Enhancer is a desktop application based on Image Processing concepts. A set of total 64 basic and complex Image Processing filters are implemented here. Moreover snapshots can be captured using video surveillance camera/webcam attached with the computer.

Features:

• Image Editing
• Image Processing

Drishti

Drishti is an open-source scientific visualisation software designed by Ajay Limaye at the National Computational Infrastructure's VizLab. The central idea of Drishti is that scientists can use it to explore and present volumetric datasets without extensive training. Drishti has been developed with its end use in mind: ie. visualising volumetric data, such as tomography data, electron-microscopy data, etc. Drishti works on GPUs with OpenGL 2.0 capability.