FPGA Based Non Uniform Illumination Correction in Image ...

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Abhishek Acharya,Rajesh Mehra,Vikram Singh Takher, Int. J. Comp. Tech. Appl., Vol 2 (2), 349-358 ISSN:2229-6093 2. Real Time Constraints In the case of real time Image processing application such as automated surveillance or radar system number of image processing stages and the biggest performance bottleneck is the time involved in processing the images captured by the camera. It is also during this preprocessing phase, when a large amount of data is being processed, that the system seeks to enhance the quality of the images captured by removing noise or unbalanced Lighting. Meeting such real-time constraints is not always possible by relying solely on a software based solution implemented on a general purpose computer (PC). This is because there are multiple constraints placed on such computers by memory and peripheral devices connected to it. This leads to explore possible hardware based alternatives such as FPGA. Field Programmable Gate Arrays (FPGA) is a reconfigurable device used to place some or all of the system onto the hardware. They allow rapid prototyping of a system and offer an inexpensive option to validate system requirements [2]. Placing the functionality of image processing applications onto hardware allows faster processing as it is no longer necessary to split the individual instructions into fetch, decode and apply cycle needed in the typical processing unit of a computer [3]. Moreover, the parallelism inherent in most low-level image processing operations can now be exploited to its full extent as they can be partitioned into subsystems, all of which can run concurrently with each other. Hence the goal of our research will be to implement the image processing algorithms developed for our surveillance system so that the processing speed of the components of the system is bounded within a specified timing constraint considered typical of such systems. 3. Non Uniform Illumination Illumination is one of the most important factors affecting the appearance of an image. It often leads to diminished structures or inhomogeneous intensities of the image due to different texture of the object surface and the shadows cast from different light source directions. On the other hand, uneven background, also known as background bias, background intensity in-homogeneity, or nonuniform background, is the problem that an ideal image f is corrupted by an uneven background signal b so that the observed image I = f +b. Recovering f from I is not an easy task when b is non-uniform. In essence, both the varying illumination and the uneven background are inhomogeneous intensity patterns that are either multiplicative or additive. A common issue irrespective of the type of camera and method of microscope attachment is uneven illumination at the edges of the image, otherwise known as vignetting. This may be attributed to multiple factors from the illumination filament, the design of the light path between the camera and the microscope, or the behavior of the imaging device. Conventional digital cameras—for example, are not designed for microscopy imaging, and many of their automatic functions can interfere with the correct aperture and exposure settings. Common problem irrespective of the type of camera and method of microscope attachment is uneven illumination at the edges of the image, known as vignetting (Figure 1, 2) 350
Abhishek Acharya,Rajesh Mehra,Vikram Singh Takher, Int. J. Comp. Tech. Appl., Vol 2 (2), 349-358 ISSN:2229-6093 narrow histogram, thereby achieving contrast enhancement. In histogram equalization (HE), the goal is to obtain a uniform histogram for the output image, so that an ―optimal‖ overall contrast is perceived. However, the feature of interest in an image might need enhancement locally. And although there was no decrease in delectability of simulated low contrast live metastases for an experienced reader [4], radiologists always find the appearance of the HE enhanced images to be objectionable in that they often introduce undesirable artifacts and noise [6]. Figure 1: Image Suffering from Un-even illumination (Grey Level Image size - 512x512) Figure 2: Image Suffering from Un-even illumination (Color Image size - 512x512) 4. Adaptive Histogram Equalization The histogram of an image represents the relative frequency of occurrence of gray levels within an image. Histogram modeling techniques modify an image so that its histogram has a desired shape. This is useful in stretching the lowcontrast levels of an image with a For images which contain local regions of low contrast bright or dark regions, global histogram equalization won't work effectively. A modification of histogram equalization called the Adaptive Histogram Equalization can be used on such images for better results. Adaptive histogram equalization works by considering only small regions and based on their local Cumulative Density Function, performs contrast enhancement of those regions. Adaptive Histogram Equalization (AHE) computes the histogram of a local window centered at a given pixel to determine the mapping for that pixel, which provides a local contrast enhancement. However, the enhancement is so strong that two major problems can arise: noise amplification in ―flat‖ regions of the image and ―ring‖ artifacts at strong edges [09]. A generalization of AHE, contrast limiting AHE (CLAHE) has more flexibility in choosing the local histogram mapping function. By selecting the clipping level of the histogram, undesired noise amplification can be reduced. In addition, by method of background 351
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