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:
yielding a transformed variance stabilized image by performing, using a variance-stabilizing transformation module, a variance stabilizing transformation to the raw image;
processing, using an image representation stage, the transformed variance stabilized image to produce a midway channel;
inverse transforming the midway channel to produce intermediate data; and
outputting the intermediate data to be processed by an image analysis network to determine a perceived classification of the raw image.
2 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
process, using a cascade of image representation stages, the transformed variance stabilized image to produce the midway channel.
3 . The processing system of claim 1 , 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.
4 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
yield the transformed variance stabilized image by replacing a pixel of value p in the raw image with a pixel of value Â(p), wherein Â(p)=2(p+⅜) 1/2 .
5 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
inverse transform the midway channel by replacing a pixel of value q of the midway channel with a pixel of value Ä(q), wherein Ä(q)=(0.25q 2 −0.125)−σ 2 +(0.3062q −1 −1.375q −2 +0.7665q −3 ).
6 . The processing system of claim 1 , wherein the at least one processor is further programmed to:
produce an additional channel based on the variance stabilizing transformation.
7 . The processing system of claim 6 , wherein the at least one processor is further programmed to:
process, using the image representation stage, the transformed variance stabilized image and the additional channel to produce a plurality of midway channels;
inverse transform each of the plurality of midway channels to produce multi-channel representation of the raw image; and
output the multi-channel representation to be processed by the image analysis network to determine the perceived classification of 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 and a variance-stabilizing transformation module;
receive a raw image; and
process the raw image through the image representation network by:
yielding a transformed variance stabilized image by performing, using the variance-stabilizing transformation module, a variance stabilizing transformation to the raw image;
processing, using the image representation stage, the transformed variance stabilized image to produce a midway channel;
inverse transforming the midway channel to produce intermediate data; and
outputting the intermediate data to be processed by an image analysis network to determine a perceived classification of the raw image.
9 . The learning machine of claim 8 , wherein the at least one processor is further programmed to:
process, using a cascade of image representation stages, the transformed variance stabilized image to produce the midway channel.
10 . The learning machine of claim 8 , 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.
11 . The learning machine of claim 8 , wherein the at least one processor is further programmed to:
yield the transformed variance stabilized image by replacing a pixel of value p in the raw image with a pixel of value Â(p), wherein Â(p)=2 (p+⅜) 1/2 .
12 . The learning machine of claim 8 , wherein the at least one processor is further programmed to:
inverse transform the midway channel by replacing a pixel of value q of the midway channel with a pixel of value Ä(q), wherein Ä(q)=(0.25q 2 −0.125)−σ 2 +(0.3062q −1 −1.375q −2 +0.7665q −3 ).
13 . The learning machine of claim 8 , wherein the at least one processor is further programmed to:
produce an additional channel based on the variance stabilizing transformation.
14 . The learning machine of claim 13 , wherein the at least one processor is further programmed to:
process, using the image representation stage, the transformed variance stabilized image and the additional channel to produce a plurality of midway channels;
inverse transform each of the plurality of midway channels to produce multi-channel representation of the raw image; and
output the multi-channel representation to be processed by the image analysis network to determine the perceived classification of the raw image.
15 . A method of machine learning, the method comprising:
receiving a raw image; and
processing the raw image through an image representation network by:
yielding a transformed variance stabilized image by performing, using a variance-stabilizing transformation module, a variance stabilizing transformation to the raw image;
processing, using an image representation stage, the transformed variance stabilized image to produce a midway channel;
inverse transforming the midway channel to produce intermediate data; and
outputting the intermediate data to be processed by an image analysis network to determine a perceived classification of the raw image.
16 . The method of claim 15 , wherein processing the transformed variance stabilized image further comprises:
processing, using a cascade of image representation stages, the transformed variance stabilized image to produce the midway channel.
17 . The method of claim 15 , 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.
18 . The method of claim 15 , wherein yielding the transformed variance stabilized image further comprises:
yielding the transformed variance stabilized image by replacing a pixel of value p in the raw image with a pixel of value Â(p), wherein Â(p)=2 (p+⅜) 1/2 .
19 . The method of claim 15 , wherein inverse transforming the midway channel further comprises:
inverse transforming the midway channel by replacing a pixel of value q of the midway channel with a pixel of value Ä(q), wherein Ä(q)=(0.25q 2 −0.125)−σ 2 +(0.3062q −1 −1.375q −2 +0.7665q −3 ).
20 . The method of claim 15 , wherein processing the raw image further comprises:
producing an additional channel based on the variance stabilizing transformation;
processing, using the image representation stage, the transformed variance stabilized image and the additional channel to produce a plurality of midway channels;
inverse transforming each of the plurality of midway channels to produce multi-channel representation of the raw image; and
outputting the multi-channel representation to be processed by the image analysis network to determine the perceived classification of the raw image.