Re: Project 1 NOISE Instructions
Posted: Thu Jan 20, 2011 11:24 am
Within the article by Leon Harmon, the concept that interested me most was the limit to which an image could be distorted while maintaining recognizable elements. Within their study, it turned out that the minimum resolution that allowed for identification of the image was 16 by 16 squares. What is more fascinating is the fact that distorting the image to a greater extent, by adding either blur or noise, can make the image more recognizable. This additive distortion process differs from the subtractive nature of lowering the resolution, but it still seems counterintuitive that an addition of random information may aid in image recognition. However, Harmon explains that the addition of noise or blur may act as a low pass filter, eliminating all high frequencies that are created by a degrading of image resolution. It is then easier to reconstruct missing information based on purely low frequencies and without high frequencies to distract and mislead the eye.
Manipulating images
In producing my images, I wanted to play with the threshold of minimum information that an image retains from its original copy. Much of my manipulation involved reducing the image to as low as 4x4 pixels, and then stretching that information to as large as 4000x4000 in size. I repeated this step many times on each image, also decreasing either the width or the height to a low amount of pixels while the opposing parameter retained a high amount of pixels. Needless to say, these manipulations would cause the image to change drastically. In 3 of the five images, there was a point at which the image appeared as a uniform color (either brown or grey). However, true to the Harmon article, the addition of noise and blur would often cause recognizable elements found within the original image to reappear in the distorted version. Thus, there are ways to degrade the image and rid it of almost all of its information, but bring back some of its original information with the addition of noise and blur. I thought that these processes of simultaneous destruction and recreation were fascinating. It was also interesting to see completely new forms appear that were not a part of the original image and to attempt to derive some type of structure and form from the addition of random information.
1. Squares
Original size: 500x333
Size: 500x3333 (bilinear)
Noise: Gaussian, 9.5%
Size: 100x100 (bicubic)
Blur: Gaussian, 3.8 pixel radius
Noise: Gaussian, 9.5%
Size: 100x1 (bicubic)
Size: 100x1000 (bicubic)
Size: 5x5 (bicubic sharper)
Size: 1000x1000 (nearest neighbor)
Noise: Gaussian, 9.5%
Noise: Gaussian, 9.5%
Noise: Gaussian, 9.5%
Noise: Gaussian, 9.5%
Size: 333x555 (bicubic)
Size: 2000x3942 (bicubic)
Blur: Gaussian, 32.8 pixel radius
Noise: Gaussian, 345.9%
Size: 4x7 (bicubic)
Size: 3000x4218 (bicubic)
Levels: Composite channel, 109,247
Levels: Gamma, 2.33
Exposure: 1.22
Offset: -0.182
Gamma Correction: 0.9
Size: 1000x1000
2. Joaquin
Original size: 445x424
Lightness: 39
Noise: Gaussian, 400%
Size: 1x424 (bicubic)
Size: 1080x424 (bicubic)
Rotate Canvas: -90°
Add Noise: Gaussian, 51.9%
Image Size: 1x424 (bicubic)
Image Size: 1080x424 (bicubic)
Image Size: 1080x1 (bicubic)
Add Noise: Gaussian, 51.9%
Image Size: 1080x1080 (bicubic)
Image Size: 10x1080 (bicubic)
Image Size: 10x10 (bicubic)
Image Size: 100x100(bicubic)
Blur: Gaussian, 3.2 pixel radius
Add Noise: Gaussian, 51.9%
Size: 20x100 (bicubic)
Size: 4x4 (bicubic)
Add Noise: Gaussian, 51.9%
Size: 4000x4000 (bicubic)
Rotate Canvas: 90°
Size: 40x400 (bicubic)
Add Noise: Gaussian, 51.9%
Size: 4000x400 (bicubic)
Size: 4000x4000 (bicubic)
Blur: Gaussian, 1.6 pixel radius
Blur: Gaussian, 1.6 pixel radius
Add Noise: Gaussian, 51.9%
Size: 5.5x4000 (bicubic)
Size: 55x4000 (bicubic)
Size: 5x5 (bicubic)
Size: 1000x1000 (bicubic)
3. Structure
Original size: 3648x2376 Project 1: Noise and Form
Add Noise: Gaussian, 51.9%
Size: 500x500 (nearest neighbor)
Size: 3000x3000 (bicubic)
Size: 30x50 (bicubic)
Blur: Gaussian, 0.9 pixel radius
Levels: composit channel, 55,188
Size: 4000x4000 (bicubic)
Flip Canvas: Vertical
Add Noise: Gaussian, 70.7%
Size: 4000x3000 (bicubic sharper)
Size: 40x30 (bicubic)
Add Noise: Gaussian, 70.7%
Size: 20x30 (bicubic)
Levels: composit channel, 56,199
Size: 5x30 (bicubic)
Size: 50x30 (bicubic)
Size: 50x3 (bicubic)
Size: 50x39 (bicubic)
Size: 3000x3000 (nearest neighbor)
Levels: composit channel, 29,184
Gamma: 0.9
4. Futbol
Original size: 416x594
Size: 1000x1900 (nearest neighbor)
Levels: composite channel, 61,239
Gamma: 0.73
Size: 100x1900 (bilinear)
Noise: Gaussian, 2.5%
Noise: Gaussian, 2.5%
Size: 100x190 (nearest neighbor)
Size: 90x90 (nearest neighbor)
Levels: composite channel, 43,259
Gamma:1.33
Size: 30x30 (nearest neighbor)
Size: 3000x3000 (nearest neighbor)
Size: 3000x100 (nearest neighbor)
Size: 4000x1900 (bilinear)
Size: 30x1900 (bicubic)
Size: 3000x1900 (bicubic)
Size: 25x3000 (bicubic)
Size: 2500x3000 (nearest neighbor)
Noise: Gaussian, 55.6%
Size: 25x30 (bilinear)
Levels: composite channel, 54,203
Gamma: 1.44
Size: 4000x3000 (nearest neighbor)
Rotate: 90°
Size: 30x3000 (nearest neighbor)
Size: 2000x3000 (bicubic)
Levels: composite channel, 44,243
Gamma: 1.3
Size: 3000x5000 (bicubic smoother)
Size: 3000x50 (bicubic)
Size: 3000x5000 (nearest neighbor)
Noise: Gaussian, 2.5%
5. Riot
Original size: 450x298 Harmon article
Add Noise: Gaussian, 28.3%
Size: 4000x20 (nearest neighbor)
Size: 500x20 (bicubic)
Size: 4000x20 (bicubic)
Size: 4000x4000 (nearest neighbor)
Levels: composite channel, 0,158
Gamma: 0.67
Size: 40x20 (bicubic smoother)
Size: 100x100 (nearest neighbor)
Size: 4x20 (bicubic)
Blur: Gaussian, 9.5 pixel radius
Add Noise: Gaussian, 28.3%
Size: 1000x100 (bilinear)
Size: 999x6 (bilinear)
Size: 1000x1000 (nearest neighbor)
Add Noise: Gaussian, 28.3%
Size: 50x40 (bicubic)
Add Noise: Gaussian, 28.3%
Size: 3000x3000 (nearest neighbor)
Size: 3x3000 (nearest neighbor)
Size: 3000x3000 (bilinear)
Size: 30x30 (bicubic)
Add Noise: Gaussian, 28.3%
Size: 5x30 (bicubic)
Add Noise: Gaussian, 28.3%
Manipulating images
In producing my images, I wanted to play with the threshold of minimum information that an image retains from its original copy. Much of my manipulation involved reducing the image to as low as 4x4 pixels, and then stretching that information to as large as 4000x4000 in size. I repeated this step many times on each image, also decreasing either the width or the height to a low amount of pixels while the opposing parameter retained a high amount of pixels. Needless to say, these manipulations would cause the image to change drastically. In 3 of the five images, there was a point at which the image appeared as a uniform color (either brown or grey). However, true to the Harmon article, the addition of noise and blur would often cause recognizable elements found within the original image to reappear in the distorted version. Thus, there are ways to degrade the image and rid it of almost all of its information, but bring back some of its original information with the addition of noise and blur. I thought that these processes of simultaneous destruction and recreation were fascinating. It was also interesting to see completely new forms appear that were not a part of the original image and to attempt to derive some type of structure and form from the addition of random information.
1. Squares
Original size: 500x333
Size: 500x3333 (bilinear)
Noise: Gaussian, 9.5%
Size: 100x100 (bicubic)
Blur: Gaussian, 3.8 pixel radius
Noise: Gaussian, 9.5%
Size: 100x1 (bicubic)
Size: 100x1000 (bicubic)
Size: 5x5 (bicubic sharper)
Size: 1000x1000 (nearest neighbor)
Noise: Gaussian, 9.5%
Noise: Gaussian, 9.5%
Noise: Gaussian, 9.5%
Noise: Gaussian, 9.5%
Size: 333x555 (bicubic)
Size: 2000x3942 (bicubic)
Blur: Gaussian, 32.8 pixel radius
Noise: Gaussian, 345.9%
Size: 4x7 (bicubic)
Size: 3000x4218 (bicubic)
Levels: Composite channel, 109,247
Levels: Gamma, 2.33
Exposure: 1.22
Offset: -0.182
Gamma Correction: 0.9
Size: 1000x1000
2. Joaquin
Original size: 445x424
Lightness: 39
Noise: Gaussian, 400%
Size: 1x424 (bicubic)
Size: 1080x424 (bicubic)
Rotate Canvas: -90°
Add Noise: Gaussian, 51.9%
Image Size: 1x424 (bicubic)
Image Size: 1080x424 (bicubic)
Image Size: 1080x1 (bicubic)
Add Noise: Gaussian, 51.9%
Image Size: 1080x1080 (bicubic)
Image Size: 10x1080 (bicubic)
Image Size: 10x10 (bicubic)
Image Size: 100x100(bicubic)
Blur: Gaussian, 3.2 pixel radius
Add Noise: Gaussian, 51.9%
Size: 20x100 (bicubic)
Size: 4x4 (bicubic)
Add Noise: Gaussian, 51.9%
Size: 4000x4000 (bicubic)
Rotate Canvas: 90°
Size: 40x400 (bicubic)
Add Noise: Gaussian, 51.9%
Size: 4000x400 (bicubic)
Size: 4000x4000 (bicubic)
Blur: Gaussian, 1.6 pixel radius
Blur: Gaussian, 1.6 pixel radius
Add Noise: Gaussian, 51.9%
Size: 5.5x4000 (bicubic)
Size: 55x4000 (bicubic)
Size: 5x5 (bicubic)
Size: 1000x1000 (bicubic)
3. Structure
Original size: 3648x2376 Project 1: Noise and Form
Add Noise: Gaussian, 51.9%
Size: 500x500 (nearest neighbor)
Size: 3000x3000 (bicubic)
Size: 30x50 (bicubic)
Blur: Gaussian, 0.9 pixel radius
Levels: composit channel, 55,188
Size: 4000x4000 (bicubic)
Flip Canvas: Vertical
Add Noise: Gaussian, 70.7%
Size: 4000x3000 (bicubic sharper)
Size: 40x30 (bicubic)
Add Noise: Gaussian, 70.7%
Size: 20x30 (bicubic)
Levels: composit channel, 56,199
Size: 5x30 (bicubic)
Size: 50x30 (bicubic)
Size: 50x3 (bicubic)
Size: 50x39 (bicubic)
Size: 3000x3000 (nearest neighbor)
Levels: composit channel, 29,184
Gamma: 0.9
4. Futbol
Original size: 416x594
Size: 1000x1900 (nearest neighbor)
Levels: composite channel, 61,239
Gamma: 0.73
Size: 100x1900 (bilinear)
Noise: Gaussian, 2.5%
Noise: Gaussian, 2.5%
Size: 100x190 (nearest neighbor)
Size: 90x90 (nearest neighbor)
Levels: composite channel, 43,259
Gamma:1.33
Size: 30x30 (nearest neighbor)
Size: 3000x3000 (nearest neighbor)
Size: 3000x100 (nearest neighbor)
Size: 4000x1900 (bilinear)
Size: 30x1900 (bicubic)
Size: 3000x1900 (bicubic)
Size: 25x3000 (bicubic)
Size: 2500x3000 (nearest neighbor)
Noise: Gaussian, 55.6%
Size: 25x30 (bilinear)
Levels: composite channel, 54,203
Gamma: 1.44
Size: 4000x3000 (nearest neighbor)
Rotate: 90°
Size: 30x3000 (nearest neighbor)
Size: 2000x3000 (bicubic)
Levels: composite channel, 44,243
Gamma: 1.3
Size: 3000x5000 (bicubic smoother)
Size: 3000x50 (bicubic)
Size: 3000x5000 (nearest neighbor)
Noise: Gaussian, 2.5%
5. Riot
Original size: 450x298 Harmon article
Add Noise: Gaussian, 28.3%
Size: 4000x20 (nearest neighbor)
Size: 500x20 (bicubic)
Size: 4000x20 (bicubic)
Size: 4000x4000 (nearest neighbor)
Levels: composite channel, 0,158
Gamma: 0.67
Size: 40x20 (bicubic smoother)
Size: 100x100 (nearest neighbor)
Size: 4x20 (bicubic)
Blur: Gaussian, 9.5 pixel radius
Add Noise: Gaussian, 28.3%
Size: 1000x100 (bilinear)
Size: 999x6 (bilinear)
Size: 1000x1000 (nearest neighbor)
Add Noise: Gaussian, 28.3%
Size: 50x40 (bicubic)
Add Noise: Gaussian, 28.3%
Size: 3000x3000 (nearest neighbor)
Size: 3x3000 (nearest neighbor)
Size: 3000x3000 (bilinear)
Size: 30x30 (bicubic)
Add Noise: Gaussian, 28.3%
Size: 5x30 (bicubic)
Add Noise: Gaussian, 28.3%