<p>The convolved image can be considered a feature map, where each new neuron represents some indication (or lack-there-of) of a feature in some receptive field of the original image, so no it does not need to be a valid image in the output.  </p>

<p>If you specifically care for it to be an image as an output, you can do a couple of things:  </p>

<p>1) normalize the produced feature map to some set range that youre working in (0-255 or 0-1)  </p>

<p>2) make the filter a valid probability distribution, and you know the output will stay in the same range as the input (ex: Gaussian filters) </p>

CC BY-SA 4.0
2019-05-20T16:30:54.470Z
Thanks for the answer. Another question: in figure 9 it is written:" Black=negative" What has the author done in this case? Black was 0 pixel value before and then he claim that black pixels are negative pixel values. Why?
MementoMori
CC BY-SA 4.0
2019-05-21T15:09:51.627Z
@MementoMori black was 0 when referring to just drawing an image in RGB space was the context he made the comment earlier, but now youre in a feature space (not 0-1 / 0-255), so when drawing it, for simplicity he mapped all negative values to 0 (black in RBG) and positive to 1 (white in RGB) just to give you a visual. He then uses the RELU (relu(x) = max(0, x)) to eliminate all negative values to show you how the feature map looks afterwards.
mshlis
CC BY-SA 4.0
2019-05-20T12:23:23.923Z
2019-05-20T12:23:23.923Z
mshlis
<p>I think normalisation into range 0-1 is needed or at least to 5-10 cliping   because the values will become astronomical after many layers. Convolved image has a vector of features for each pixel. Take a RGB for example -> each color is a feature in one pixel, the next map will be like 'horisontal line, vertical line, circle' for one pixel surroundings.</p>

CC BY-SA 4.0
2019-06-19T15:17:34.720Z
2019-06-19T15:17:34.720Z
user8426627
<p>I'm studying convolutional neural networks from the following article <a href="https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/" rel="nofollow noreferrer">https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/</a>.</p>

<p>If we take a grayscale image, the value of the pixel will be between 0 and 255. Now, if we apply a filter to our "new image", can we have pixels whose values are not included in this range? In this case, how can we create the convolved image?</p>

<neural-networks><convolutional-neural-networks>
What are the value of the pixels of the convolved image?
MementoMori
# What are the value of the pixels of the convolved image?

(Asked by: username_0 on 2019-05-20)

I'm studying convolutional neural networks from the following article <https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/>.

If we take a grayscale image, the value of the pixel will be between 0 and 255. Now, if we apply a filter to our "new image", can we have pixels whose values are not included in this range? In this case, how can we create the convolved image?

**Tags:** neural-networks, convolutional-neural-networks


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## 2 answers

### Answer by username_1 on 2019-05-20. Score: 1

The convolved image can be considered a feature map, where each new neuron represents some indication (or lack-there-of) of a feature in some receptive field of the original image, so no it does not need to be a valid image in the output.

If you specifically care for it to be an image as an output, you can do a couple of things:

1) normalize the produced feature map to some set range that youre working in (0-255 or 0-1)

2) make the filter a valid probability distribution, and you know the output will stay in the same range as the input (ex: Gaussian filters)

> **Comments:**
> **username_0**: Thanks for the answer. Another question: in figure 9 it is written:" Black=negative" What has the author done in this case? Black was 0 pixel value before and then he claim that black pixels are negative pixel values. Why?  
> **username_1**: @username_0 black was 0 when referring to just drawing an image in RGB space was the context he made the comment earlier, but now youre in a feature space (not 0-1 / 0-255), so when drawing it, for simplicity he mapped all negative values to 0 (black in RBG) and positive to 1 (white in RGB) just to give you a visual. He then uses the RELU (relu(x) = max(0, x)) to eliminate all negative values to show you how the feature map looks afterwards.  

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### Answer by username_2 on 2019-06-19. Score: 0

I think normalisation into range 0-1 is needed or at least to 5-10 cliping because the values will become astronomical after many layers. Convolved image has a vector of features for each pixel. Take a RGB for example -> each color is a feature in one pixel, the next map will be like 'horisontal line, vertical line, circle' for one pixel surroundings.


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