In conventional processing engines, every pixel as-you-see-it is the result of the operation that was last carried out (some simple screen stretch capabilities excepted to visualise linear data). Operations are carried out one after the other and exist in some linear stack (typically accessible via an 'undo' history). The individual operations however, have no concept of what other operations preceded them, nor what operations will follow them, nor what the result was or will be. Signal flows one way in time; forward. Conventional software does not feed back signal, nor propagates it back and forth in order to refine the final result of the stack or 'undo' history.
Some software platforms even mistakenly implement astronomical signal processing in a formalised object oriented platform. An object oriented approach, by definition, implements strict decoupling of the individual operations, and formalises complete unawareness of the algorithms contained therein, with regards to where and when in the signal flow they are being invoked. This design completely destroys any ability of such algorithms to know what augmenting data or statistics may be available to them to do a better job. Worse, such software allows for entirely nonsensical signal flows that violate mathematical principles and the physics these principles are meant to model. The result is lower quality images through less sophisticated (but more numerous) algorithms, rounding errors, user-induced correction feedback loops (invoking another module to correct the output of the last), and steeper learning curves than necessary.
In contrast, StarTools works by constantly re-building and refining a single equation, for every pixel, that transforms the source data into the image-as-you-see-it. It means there is no concept of linear versus non-linear processing, there are no screen stretches with lookup tables, there is no scope for illegal sequences, there is no overcooking or noise grain/artefact propagation, there are no rounding errors. What you see is the shortest, purest transformation of your linear signal into a final image. And what you see is what you get.
Even more ground-breaking; substituting some of its variables for the equation itself (or parts thereof), allows complex feedback of signal to occur. This effectively provides, for example, standard algorithms like deconvolution or noise reduction, precise knowledge about a "future" or "past" of the signal. Such algorithms will be able to accurately calculate how the other algorithms will behave in response to their actions anywhere on the timeline. The result is that such algorithms are augmented with comprehensive signal evolution statistics and intelligence for the user's entire workflow. This lets these algorithms yield greatly superior results to that of equivalent algorithms in conventional software. Applying the latter innovation to - otherwise - standard, well known algorithms is, in fact, the subject of most of StarTools' research and development efforts.
The power of StarTools' novel engine, is not only expressed in higher signal fidelity and lifting of limitations of conventional engines; its power is also expressed in ease-of-use. Illegal or mathematically incongruent paths are closed off, while parameter tweaks always yield useful and predictable results. Defaults just work for most datasets, proving that the new engine is universally applicable, consistent and rooted in a mathematically sound signal processing paradigm.
I'm relatively new to image processing and just wanted to say how straight forward and powerful StarTools is.
Noise reduction and grain shaping is performed in three stages.
Signal evolution Tracking data mining plays a very important role in StarTools and understanding it is key to achieving superior results with StarTools.
Exclusive to the main screen, the buttons that activate the different modules, reside on the left hand side of the main screen.
A video is also available that shows a simple, short processing workflow of a real-world, imperfect dataset.
You can convert everything you see to a format you find convenient. Give it a try!