ABSTRACT :
In this paper, we present a one-dimensional (1-D) approach to the problem of image restoration. Our approach involves a cascade of four 1-D adaptive filters oriented in the four major correlation directions of the image, with each filter treating the image as a l-D signal. The objective of our 1-D approach is to improve the performance of the more general two-dimensional (2-D) approach. This differs considerably from previous 1-D approaches, the objectives of which have typically been to approximate a more general 2-D approach for computational reasons and not to improve its performance. To illustrate this point, our approach is applied to an existing 2-D image restoration algorithm. Experiments with images at low SNRs (signal to noise ratios) show that the 1-I) approach performs better than the 2-D approach for the specific image restoration algorithm. Our 1-D approach preserves edges while removing noise in all regions of the image including the edge regions.
EXISTING SYSTEM :
Many heuristic as well as mathematically optimal techniques have been proposed [1] for restoring noisy images. Earlier approaches to image restoration were mostly linear filters derived with the assumption of a stationary image model. Such an assumption leads to space invariant filters which smooth out edges because of the unavoidable compromise between noise and resolution. Many adaptive restoration systems have been proposed recently to overcome this problem. Although adaptive systems are computationally more expensive in both design and implementation, they have been observed to be significantly better in performance than non-adaptive methods. Some adaptive systems partition the image into regions or sub images in which different stationary models are assumed [2-5]. Others assume a simple image model and use a moving 2-D window to continuously estimate the model parameters and adjust a nonlinear 2-D filter.
PROPOSED SYSTEM :
Many adaptive image restoration systems apply a 2-D spatially variant filter to the degraded image. The filter is typically determined from a small local region of the image based on some simple mathematical criterion such as the mean square error minimization. Within the local region, the image is usually assumed to be a sample of a stationary random process so that methods such as Wiener filtering can be used to determine the filter coefficients. A major problem of this approach often occurs in edge regions where the signal cannot be adequately modelled even locally as a sample of a stationary random process and a filter determined with this assumption may not be able to preserve edges and reduce noise at the same time.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
