EP1S80B956C7N Altera, EP1S80B956C7N Datasheet - Page 624

• Manufacturer Part Number: EP1S80B956C7N
• Description: IC STRATIX FPGA 80K LE 956-BGA
• Manufacturer: Altera
• Series: Stratix®r

Specifications of EP1S80B956C7N

• Number Of Logic Elements/cells: 79040
• Number Of Labs/clbs: 7904
• Total Ram Bits: 7427520
• Number Of I /o: 683
• Voltage - Supply: 1.425 V ~ 1.575 V
• Mounting Type: Surface Mount
• Operating Temperature: 0°C ~ 85°C
• Package / Case: 956-BGA
• Lead Free Status / RoHS Status: Lead free / RoHS Compliant
• Number Of Gates: -

Matrix Manipulation
7–46
Stratix Device Handbook, Volume 2
Two-Dimensional Filtering & Video Imaging
FIR filtering for video applications and image processing in general is
used in many applications, including noise removal, image sharpening to
feature extraction.
For noise removal, the goal is to reduce the effects of undesirable,
contaminative signals that have been linearly added to the image.
Applying a low pass filter or smoothing function flattens the image by
reducing the rapid pixel-to-pixel variation in gray levels and, ultimately,
removing noise. It also has a blurring effect usually used as a precursor
for removing unwanted details before extracting certain features from the
image.
Image sharpening focuses on the fine details of the image and enhances
sharp transitions between the pixels. This acts as a high-pass filter that
reduces broad features like the uniform background in an image and
enhances compact features or details that have been blurred.
Feature extraction is a form of image analysis slightly different from
image processing. The goal of image analysis in general is to extract
information based on certain characteristics from the image. This is a
multiple step process that includes edge detection. The easiest form of
edge detection is the derivative filter, using gradient operators.
All of the operations above involve transformation of the input image.
This can be presented as the convolution of the two-dimensional input
image, x(m,n) with the impulse response of the transform, f(k,l), resulting
in y(m,n) which is the output image.
The f(k,l) function refers to the matrix of filter coefficients. Because the
matrix operation is analogous to a filter operation, the matrix itself is
considered the impulse response of the filter. Depending on the type of
operation, the choice of the convolutional kernel or mask, f(k,l) is
different.
a larger image.
y m n
y m n
Figure 7–26
=
=
f k l
k
=
N
–
N
l
shows an example of convolving a 3
=
N
–
N
x m n
f k l x m k n l –
–
Altera Corporation
September 2004
3 mask with