Digital image processing involves using computer algorithms to enhance and analyze digital images, providing advantages over analog methods like reduced noise and signal distortion. Its techniques were developed in the 1960s, initially for purposes such as satellite imagery and medical imaging, and have evolved significantly with technological advancements, including the introduction of various image sensors and compression methods such as JPEG. Today, digital image processing underpins many applications, from medical diagnostics to real-time video processing.

1. DIGITAL IMAGE
PROCESSING
Presented by
Gayathry Satheesan
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2. Digital image processing
• In computer science, digital image processing is the use of
computer algorithms to perform image processing on digital images.
• As a subcategory or field of digital signal processing, digital image
processing has many advantages over analog image processing.
• It allows a much wider range of algorithms to be applied to the input
data and can avoid problems such as the build-up of noise and signal
distortion during processing.
• Since images are defined over two dimensions (perhaps more) digital
image processing may be modeled in the form of multidimensional
systems.
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3. History
• Many of the techniques of digital image processing, or digital picture
processing as it often was called, were developed in the 1960s at the Jet
Propulsion Laboratory, with application to satellite imagery, wire-photo
standards conversion, medical imaging. videophone, character recognition
,and photograph enhancement.
• The purpose of early image processing was to improve the quality of the
image.
• It was aimed at human beings to improve the visual effect of people.
• In image processing, the input is a low-quality image, and the out put is an
image with improved quality.
• Common image processing include image enhancement, restoration,
encoding, and compression.
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4. • The first successful application was the American Jet Propulsion
Laboratory (JPL).
• They used image processing techniques such as geometric
correction, gradation transformation, noise removal, etc. on the
thousands of lunar photos sent back by the Space Detector Ranger
7 in 1964, taking into account the position of the sun and the
environment of the moon.
• The impact of the successful mapping of the moon's surface map
by the computer has been a huge success.
•
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5. • Later, more complex image processing was performed on the
nearly 100,000 photos sent back by the spacecraft, so that the
topographic map, color map and panoramic mosaic of the moon
were obtained, which achieved extraordinary results and laid a
solid foundation for human landing on the moon.
• Roliferate as affordable computers and dedicated hardware were
becoming available.
• This led to images being processed in real-time, for some
dedicated problems such as television standards conversion.
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6. • As general-purpose computers became faster, they started to take
over the role of dedicated hardware for all but the most
specialized and computer-intensive operations.
• With the fast computers and signal processors available in the
2000s, digital image processing has become the most common
form of image processing, and is generally used because it is not
only the most versatile method, but also the cheapest.
• Digital image processing technology for medical applications was
inducted into the Space Foundation Space Technology Hall of
Fame in 1994.[4]
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7. • Image sensors
• The basis for modern image sensors is metal-oxide-
semiconductor (MOS) technology, which originates from the invention
of the MOSFET (MOS field-effect transistor) by Mohamed
M,Atalla and Dawon Kahng at Bell Labs in 1959.
• This led to the development of digital semiconductor image sensors,
including the charge-coupled device (CCD) and later the CMOS sensor.
• The charge-coupled device was invented by Willard S.
Boyle and George E. Smith at Bell Labs in 1969.
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8. • While researching MOS technology, they realized that an electric
charge was the analogy of the magnetic bubble and that it could be
stored on a tiny MOS capacitor.
• As it was fairly straight forward to fabricate a series of MOS
capacitors in a row, they connected a suitable voltage to them so that
the charge could be stepped along from one to the next.
• The CCD is a semiconductor circuit that was later used in the
first digital video cameras for television broadcasting.
• The NMOS active-pixel sensor (APS) was invented by Olympus in
Japan during the mid-1980s.
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9. • This was enabled by advances in MOS semiconductor device
fabrication, with MOSFET scaling reaching smaller micron and then
sub-micron levels.
• The NMOS APS was fabricated by Tsutomu Nakamura's team at
Olympus in 1985.
• The CMOS active-pixel sensor (CMOS sensor) was later developed
by Eric Fossum's team at the NASA Jet Propulsion Laboratory in
1993.By 2007, sales of CMOS sensors had surpassed CCD sensors.
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10. • Image compression
• An important development in digital image compression technology was
the discrete cosine transform (DCT), a lossy compression technique first
proposed by Nasir Ahmed in 1972.
• DCT compression became the basis for JPEG, which was introduced by
the Joint Photographic Experts Group in 1992.
• JPEG compresses images down to much smaller file sizes, and has
become the most widely used image file format on the Internet.
• Its highly efficient DCT compression algorithm was largely responsible
for the wide proliferation of digital images and digital photos,with
several billion JPEG images produced every day as of 2015.
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11. • Digital signal processor (DSP)
• Electronic signal processing was revolutionized by the wide adoption
of MOS technology in the 1970s.
• MOS integrated circuit technology was the basis for the first single-
chip microprocessors and microcontrollers in the early 1970s, and then
the first single-chip digital signal processor (DSP) chips in the late 1970s.
• DSP chips have since been widely used in digital image processing.
• The discrete cosine transform (DCT) image compression algorithm has
been widely implemented in DSP chips, with many companies
developing DSP chips based on DCT technology.
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12. • DCTs are widely used for encoding, decoding, video coding, audio
coding, multiplexing, control signals, signaling, analog-to-digital
conversion, formatting luminance and color differences, and color
formats such as YUV444 and YUV411.
• DCTs are also used for encoding operations such as motion
estimation, motion compensation, inter-frame prediction, quantization,
perceptual weighting, entropy encoding, variable encoding, and motion
vectors, and decoding operations such as the inverse operation between
different color formats (YIQ, YUV and RGB) for display purposes.
• DCTs are also commonly used for high-definition television (HDTV)
encoder/decoder chips
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13. Digital image transformations
• Filtering
• Digital filters are used to blur and sharpen digital images.
Filtering can be performed by:
• Convolution with specifically designed kernels (filter array) in
the spatial domain masking specific frequency regions in the
frequency (Fourier) domain
• The following examples show both methods:
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17. Image padding in Fourier domain filtering
•Images are typically padded before being transformed to the Fourier
space, the high pass filtered images below illustrate the consequences
of different padding techniques:
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18. Filtering Code Examples
•MATLAB example for spatial domain high pass filtering.
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19. Affine transformations
Affine transformations enable basic image transformations including
scale, rotate, translate, mirror and shear as is shown in the following
examples:
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21. • To apply the affine matrix to an image, the image is converted to
matrix in which each entry corresponds to the pixel intensity at
that location.
• Then each pixel's location can be represented as a vector
indicating the coordinates of that pixel in the image, [x, y], where
x and y are the row and column of a pixel in the image matrix.
• This allows the coordinate to be multiplied by an affine-
transformation matrix, which gives the position that the pixel
value will be copied to in the output image.
• However, to allow transformations that require translation
transformations, 3 dimensional homogeneous coordinates are
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22. • However, to allow transformations that require translation
transformations, 3 dimensional homogeneous coordinates are needed.
• The third dimension is usually set to a non-zero constant, usually 1, so
that the new coordinate is [x, y, 1].
• This allows the coordinate vector to be multiplied by a 3 by 3 matrix,
enabling translation shifts.
• So the third dimension, which is the constant 1, allows translation.
• Because matrix multiplication is associative, multiple affine
transformations can be combined into a single affine transformation by
multiplying the matrix of each individual transformation in the order
that the transformations are done.
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23. • This results in a single matrix that, when applied to a point vector, gives
the same result as all the individual transformations performed on the
vector [x, y, 1] in sequence.
• Thus a sequence of affine transformation matrices can be reduced to a
single affine transformation matrix.
• For example, 2 dimensional coordinates only allow rotation about the
origin (0, 0). But 3 dimensional homogeneous coordinates can be used to
first translate any point to (0, 0), then perform the rotation, and lastly
translate the origin (0, 0) back to the original point (the opposite of the
first translation).
• These 3 affine transformations can be combined into a single matrix, thus
allowing rotation around any point in the image
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24. Applications
• Digital camera images
• Digital cameras generally include specialized digital image processing
hardware – either dedicated chips or added circuitry on other chips – to
convert the raw data from their image sensor into a color-
corrected image in a standard image file format.
• Film
• Westworld (1973) was the first feature film to use the digital image
processing to pixellate photography to simulate an android's point of
view.
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25. The End!!
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