SYSTEM AND METHOD FOR END-TO-END DIFFERENTIABLE JOINT IMAGE REFINEMENT AND PERCEPTION
System and method for end-to-end differentiable joint image refinement and perception are provided. A learning machine employs an image acquisition device for acquiring a set of training raw images. A processor determines a representation of a raw image, initializes a set of image representation parameters, defines a set of analysis parameters of an image analysis network configured to process the image's representation, and jointly trains the set of representation parameters and the set of analysis parameters to optimize a combined objective function. A module for transforming pixel-values of the raw image to produce a transformed image comprising pixels of variance-stabilized values, a module for successively performing processes of soft camera projection and image projection, and a module for inverse transforming the transformed pixels are disclosed. The image projection performs multi-level spatial convolution, pooling, subsampling, and interpolation.
1 . A processing system comprising:
at least one memory device having a computer readable instructions stored thereon; and
at least one processor coupled to the at least one memory device and configured to execute the computer readable instructions, upon execution of the computer readable instructions, the at least one processor programmed to:
receive a raw image; and
process the raw image through an image representation network by:
producing an additional channel corresponding the raw image;
processing, using an image representation stage, the raw image and the additional channel; and
outputting a multi-channel representation of the raw image to be processed by an image analysis network to determine an image classification.
2 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
yield a transformed variance stabilized image by performing, using a variance-stabilizing transformation module, a variance stabilizing transformation to the raw image;
process, using the image representation stage, the transformed variance stabilized image and the additional channel to produce midway channels; and
inverse transform the midway channels to produce the multi-channel representation.
3 . The processing system of claim 2 , wherein the at least one processor is further programmed to:
produce the additional channel based on a resulting noise parameter in the variance stabilizing transformation.
4 . The processing system of claim 2 , wherein the at least one processor is further programmed to:
yield the transformed variance stabilized image by performing an Anscombe's transformation to the raw image.
5 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
shape the raw image to produce an image of a reduced coefficient of variation by applying a monotone increasing transformation function to the raw image.
6 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
shape the raw image to produce an image of a reduced coefficient of variation by applying a linear transformation function to the raw image.
7 . The processing system of claim 6 , wherein the at least one processor is further programmed to:
adjust a bias and/or a slope of the linear transformation function; and
shape the raw image by applying the linear transformation function with the adjusted bias and/or the adjusted slope to the raw image.
8 . A learning machine comprising:
at least one memory device having a computer readable instructions stored thereon; and
at least one processor coupled to the at least one memory device and configured to execute the computer readable instructions, upon execution of the computer readable instructions, the at least one processor programmed to:
form an image representation network, the image representation network including an image representation stage;
receive a raw image; and
process the raw image through the image representation network by:
producing an additional channel corresponding the raw image;
processing, using the image representation stage, the raw image and the additional channel; and
outputting a multi-channel representation of the raw image to be processed by an image analysis network to determine an image classification.
9 . The learning machine of claim 8 , wherein the image representation network includes a variance-stabilizing transformation module and an inverse transformation module, the at least one processor further programmed to:
yield a transformed variance stabilized image by performing, using a variance-stabilizing transformation module, a variance stabilizing transformation to the raw image;
process, using the image representation stage, the transformed variance stabilized image and the additional channel to produce midway channels; and
inverse transform, using the inverse transformation module, the midway channels to produce the multi-channel representation.
10 . The learning machine of claim 9 , wherein the at least one processor is further programmed to:
produce the additional channel based on a resulting noise parameter in the variance stabilizing transformation.
11 . The learning machine of claim 9 , wherein the at least one processor is further programmed to:
yield the transformed variance stabilized image by performing an Anscombe's transformation to the raw image.
12 . The learning machine of claim 8 , wherein the at least one processor is further programmed to:
shape the raw image to produce an image of a reduced coefficient of variation by applying a monotone increasing transformation function to the raw image.
13 . The learning machine of claim 8 , wherein the at least one processor is further programmed to:
shape the raw image to produce an image of a reduced coefficient of variation by applying a linear transformation function to the raw image.
14 . The learning machine of claim 13 , wherein the at least one processor is further programmed to:
adjust a bias and/or a slope of the linear transformation function; and
shape the raw image by applying the linear transformation function with the adjusted bias and/or the adjusted slope to the raw image.
15 . A method of generating a multi-channel representation of a raw image, the method comprising:
receiving a raw image; and
processing the raw image through an image representation network by:
producing an additional channel corresponding the raw image;
processing, using an image representation stage, the raw image and the additional channel; and
outputting a multi-channel representation of the raw image to be processed by an image analysis network to determine an image classification.
16 . The method of claim 15 , wherein processing the raw image further comprises:
yielding a transformed variance stabilized image by performing, using a variance-stabilizing transformation module, a variance stabilizing transformation to the raw image;
processing, using the image representation stage, the transformed variance stabilized image and the additional channel to produce midway channels; and
inverse transforming the midway channels to produce the multi-channel representation.
17 . The method of claim 16 , wherein producing the additional channel further comprises:
producing the additional channel based on a resulting noise parameter in the variance stabilizing transformation.
18 . The method of claim 16 , wherein yielding the transformed variance stabilized image further comprises:
yielding the transformed variance stabilized image by performing an Anscombe's transformation to the raw image.
19 . The method of claim 15 , wherein processing the raw image further comprises:
shaping the raw image to produce an image of a reduced coefficient of variation by applying a monotone increasing transformation function to the raw image.
20 . The method of claim 15 , wherein processing the raw image further comprises:
shaping the raw image to produce an image of a reduced coefficient of variation by applying a linear transformation function to the raw image.