A Fuzzy Impulse Noise Detection and Reduction Method

Abstract— Removing or reducing impulse noise is a very active
research area in image processing. In this paper we describe a
new algorithm that is especially developed for reducing all kinds
of impulse noise: FIDRM (Fuzzy Impulse noise Detection and
Reduction Method). It can also be applied to images having a
mixture of impulse noise and other types of noise. The result
is an image quasi without (or with very little) impulse noise
so that other filters can be used afterwards. This nonlinear
filtering technique contains two separated steps: an impulse
noise detection step and a reduction step that preserves edge
sharpness. Based on the concept of fuzzy gradient values our
detection method constructs a fuzzy set impulse noise. This
fuzzy set is represented by a membership function that will
be used by the filtering method, which is a fuzzy averaging
of neighbouring pixels. Experimental results show that FIDRM
provides a significant improvement on other existing filters.
FIDRM is not only very fast but also very effective for reducing
little as well as very high impulse noise.

Abstract: In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy
relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and
artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel
magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges.

An image can be considered as a fuzzy subset of plane. Fuzzy entropy measuring the blur
in an image is a functional which increases when the sharpness of its argument image
decreases. In this paper we consider three parameters such as the intensification parameter t,
the fuzzifier
h
f and crossover point
c
 .The partially ordered set and lattice theory defined in
section 2 strengthens the selection of any value in the interval [0, 1] such that the image can
be enhanced. Here we verify the approximate value of
c
 and visual observation image
enhancement in different figures. By entropy minimization technique we construct an
approximation of the ideal image and for that we apply the enhancement operation to the low
contrast image. This can be done by an optimization process that minimizes a criterion
function.

This paper presents an image-processing-based fuzzy auto-
pilot scheme for accomplishing small-boat approaching
maneuvers in a harbor environment. In the proposed approach,
two canvas targets are arranged in cascade on the quayside to
form a leading line. The targets are detected by a charge
coupled device (CCD) camera mounted on the bow of the boat,
and their geometric centers are computed by a Hue- and
Saturation-based image-processing scheme. The autopilot
system calculates the current heading deviation and tracking
deviation angles of the boat by analyzing the displacements of
the target centers relative to the CCD center line. These angles
are then supplied to a fuzzy-logic-based control system to
determine the rudder commands required to bring the boat
back on course. During the approaching maneuver, the auto-
pilot system estimates the distance between the boat and the
quayside using a simple trigonometric relationship, and at a
certain pre-defined distance, automatically switches the steer-
ing control system from an approach mode to a berthing
control mode. The experimental results obtained using a small
FRP boat confirm the ability of the autopilot system to
accomplish the approaching maneuver and show that the
estimated value of the boat-to-quayside distance deviates by
approximately 10~20% from the exact value.

²This paper introduces a parallelization of fuzzy logic-
based image processing using Graphics Processor Units (GPUs).
Using an NVIDIA 8800 Ultra, a 126 time speed improvement can
be made to fuzzy edge extraction making its processing real-time.
The GPU can process approximately 42 frames per second at
640x480 image resolution, thus 307,200 inference processes per
frame. With a computational speed improvement of over two
orders of magnitude, more time can be allocated to higher level
computer vision algorithms. This GPU solution is described
using NVIDIAs Compute Unified Device Architecture (CUDA).

In many image processing applications the image quality
should be improved to support the human perception.
The image quality evaluation by the human observers is,
however, heavily subjective in the nature. Different
observers judge the image quality differently. In many
cases the relevant part of image information which is
perceived by the observer should reach a maximum. In
this work we present a new approach to image
enhancement which is based on fusion of different
algorithms. We use fuzzy measure theory to represent the
human subjectivity, and fuzzy integrals to aggregate this
subjectivity with objective criteria. We also apply the
Dempster aggregation rule to define a degree of
compromise. Finally, we use a fuzzy rule-based approach
to construct an aggregation matrix that allow us to
generate enhanced images for each individual observer.
As an example, we apply this approach to increase the
quality of portal images that are used in radiation
therapy.