**3. Preliminary investigation of image denoising**

Noise reduction is the process of removing noise from image. Noise reduction techniques are conceptually very similar regardless of the signal being processed, however a priori knowledge of the characteristics of an expected signal can mean the implementations of techniques vary greatly depending on the type of signal. Noise can be random or white noise with no coherence or coherent noise introduced by the device mechanism or processing algorithms. In photographic film and magnetic tape, noise (both visible and audible) is introduced due to the grain structure of the medium. In photographic film, the size of the grains in the film determines the film's sensitivity, more sensitive film having larger sized grains. In magnetic tape, the larger the grains of the magnetic particles (usually ferric oxide or magnetite), the more prone the medium is to noise. To compensate for this, larger areas of film or magnetic tape may be used to lower the noise to an acceptable level.

#### **3.1 Noise study**

Estimating the noise level from a single image seems like an impossible task: the image should be recognized whether local image variations are due to color, texture, or lighting variations from the image itself, or due to the noise. It might seem that accurate estimation of the noise level would require a very sophisticated prior model for images. Capturing a pinhole image (large depth-of-field) is important to many computer vision applications, such as 3D reconstruction, motion analysis, and video surveillance. For a dynamic scene, capturing pinhole images however is difficult: we have often to make a tradeoff between depth-of-field and motion blur. For example, if a large aperture and short exposure to avoid motion blur, the resulting images will have small depth-of-field; otherwise, if we use a small aperture and long exposure, the depth-of-field will be large, but at the expense of motion blur.

#### **3.2 Potential disadvantages of noise filters in images**

Many types of distortions limit the quality of digital images during image acquisition, formation, storage and transmission. Often, images are corrupted by impulse noise. The intensity of impulse noise has the tendency of being either relatively high or low thereby causing loss of image details. It is important to eliminate noise in images before using them for other image processing techniques like edge detection, segmentation, registration etc. Several filtering methods have been proposed in the past to address impulse noise removal(Wang &Hang 1999). One of the more famous filters for gray scale images is the standard median filter which rank orders the pixel intensities within a filtering window and replaces the center pixel with the median value. Extending the idea of a scalar median filter to color images is not straightforward due to the lack of a natural concept of ranking among the vectors. Color distortion may occur when the scalar median filter is applied separately to every single component of the color vectors. A method called Vector Median Filter (VMF) which considers all the three color components and rank orders the vectors. Various modifications of the standard VMF have been introduced like Directional Median Filter and Central Weighted Vector Median Filter. The biggest drawback of the conventional vector median approaches is that they apply median operation to each pixel, irrespective of it being corrupted or not. An intuitive solution to overcome this disadvantage is to first detect the

Noise reduction is the process of removing noise from image. Noise reduction techniques are conceptually very similar regardless of the signal being processed, however a priori knowledge of the characteristics of an expected signal can mean the implementations of techniques vary greatly depending on the type of signal. Noise can be random or white noise with no coherence or coherent noise introduced by the device mechanism or processing algorithms. In photographic film and magnetic tape, noise (both visible and audible) is introduced due to the grain structure of the medium. In photographic film, the size of the grains in the film determines the film's sensitivity, more sensitive film having larger sized grains. In magnetic tape, the larger the grains of the magnetic particles (usually ferric oxide or magnetite), the more prone the medium is to noise. To compensate for this, larger areas of film or magnetic tape may be used to lower the noise to an acceptable level.

Estimating the noise level from a single image seems like an impossible task: the image should be recognized whether local image variations are due to color, texture, or lighting variations from the image itself, or due to the noise. It might seem that accurate estimation of the noise level would require a very sophisticated prior model for images. Capturing a pinhole image (large depth-of-field) is important to many computer vision applications, such as 3D reconstruction, motion analysis, and video surveillance. For a dynamic scene, capturing pinhole images however is difficult: we have often to make a tradeoff between depth-of-field and motion blur. For example, if a large aperture and short exposure to avoid motion blur, the resulting images will have small depth-of-field; otherwise, if we use a small aperture and long exposure, the depth-of-field will be large, but at the expense of motion

Many types of distortions limit the quality of digital images during image acquisition, formation, storage and transmission. Often, images are corrupted by impulse noise. The intensity of impulse noise has the tendency of being either relatively high or low thereby causing loss of image details. It is important to eliminate noise in images before using them for other image processing techniques like edge detection, segmentation, registration etc. Several filtering methods have been proposed in the past to address impulse noise removal(Wang &Hang 1999). One of the more famous filters for gray scale images is the standard median filter which rank orders the pixel intensities within a filtering window and replaces the center pixel with the median value. Extending the idea of a scalar median filter to color images is not straightforward due to the lack of a natural concept of ranking among the vectors. Color distortion may occur when the scalar median filter is applied separately to every single component of the color vectors. A method called Vector Median Filter (VMF) which considers all the three color components and rank orders the vectors. Various modifications of the standard VMF have been introduced like Directional Median Filter and Central Weighted Vector Median Filter. The biggest drawback of the conventional vector median approaches is that they apply median operation to each pixel, irrespective of it being corrupted or not. An intuitive solution to overcome this disadvantage is to first detect the

**3. Preliminary investigation of image denoising** 

**3.2 Potential disadvantages of noise filters in images** 

**3.1 Noise study** 

blur.

corrupt pixels and then to apply filtering on those pixels alone (Trygve &Hakon 1999). One of the main problems with impulse noise detection is that it is difficult to differentiate between an edge and an impulse noise. In the intensity space, both these stand as peaks in their neighborhood. The difference between the center pixel with the minimum and maximum gray value in the filtering window is taken and if greater than a certain threshold, the center pixel is considered as noise. The disadvantage of this method is that the false positive rate is very high and most of the edges also get detected as noise. Coherent processing of synthetic aperture radar (SAR) data makes images susceptible to speckles (Lee,Jukervish 1994). Basically, the speckles are signal-dependent and, therefore, act like multiplicative noise. This report develops a statistical technique to define a noise model, and then successfully applies a local statistics noise filtering algorithm to a set of actual SEASAT SAR images. The smoothed images permit observers to resolve fine detail with an enhanced edge effect. Several SEASAT SAR images are used for demonstration.

The standard algorithm shows very good performance removing the additive noise. In SAR images, on the contrary, the noise is multiplicative. In particular three procedures are there to obtain the speckle noise reduction:


Regarding the first point, the logarithmic transformation allowed to apply the standard fuzzy filtering algorithm obtaining a significant removal of the multiplicative noise.

The repeated application of the filtering algorithm permits to reduce the speckle noise granularity without degradation of the sharpness. This is very important for the subsequent recognition of the pattern present in the image.
