Bin: Trade Resolution for Noise Reduction

With today’s multi-megapixel imaging equipment and high density CCDs, oversampling is a common occurrence; there is only so much detail that seeing conditions allow for with a given setup. Beyond that it is impossible to pick up fine detail. Once detail no longer fits in a single pixel, but instead gets “smeared out” over multiple pixels due to atmospheric conditions (resulting in a blur), binning may turn this otherwise useless blur into noise reduction. Binning your data may make an otherwise noisy and unusable data set usable again, at the expense of 'useless' resolution.

The Bin module was created to provide a freely scalable alternative to the fixed 2×2 (4x reduction in resolution) or 4×4 (16x reduction in resolution) software binning modes commonly found in other software packages or modern consumer digital cameras and DSLRs (also known as ‘Low Light Mode’). As opposed to these other binning solutions, the StarTools' Bin module allows you to bin your data (and gain noise reduction) by the amount you want – if your data is seeing-limited (blurred due to adverse seeing conditions) you are now free to bin your data until exactly that limit and you are not forced by a fixed 2×2 or 4×4 mode to go beyond that.

Similarly, deconvolution (and subsequent recovery of detail that was lost due to atmospheric conditions) may not be a viable proposition due to the noisiness of an initial image. Binning may make deconvolution an option again. The StarTools Bin module allows you to determine the ratio whith which you use your oversampled data for binning and deconvolution to achieve a result that is finely tuned to your data and imaging circumstances of the night(s).

Core to StarTools' fractional binning algorithm is a custom built anti-aliasing filter that has been carefully designed to not introduce any ringing (overshoot) and, hence, to not introduce any artefacts when subsequent deconvolution is used on the binned data.

400% zoomed crop of an image that has been scaled down to 35% of its original size using nearest neighbor sampling (retaining noise).

400% zoomed crop of an image that has been binned to 2.83x2.83 (binned down to 35% of its original size). A significant amount of noise reduction has occured. Futher deconvolution is now an option. Notice real structural detail is not compromised, but any non-structural detail (noise) has been removed.