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    1. OVERVIEW: 1.1 INTRODUCTION When a photograph is taken in low light conditions or of a fast moving object, motion blur can cause significant degradation of the image. This is caused by the movement of the object relative to the sensor in the camera during the time the shutter is open. Both the object moving and camera shake contribute to this blurring. The  problem is particularly apparent in low light conditions when the exposure time can often be in the region of several seconds. There are several techniques for either  preventing image motion blurring at the time of image capture or post processing images to remove motion blur. s well as in everyday photography, the problem is  particularly important to applications such as video surveillance where low quality cameras are used to capture sequences of photographs of moving objects !usually  people .#urrent techniques can be split roughly into the following categories$ % &ardware in the optical system of the camera to stabili'e the image % (ost processing of the image to remove motion blur by estimating the camera)s motion % *rom a single photograph !blind deconvolution  % *rom a sequence of photographs *igure+$ otion Blur  +  1.2 AIM OF PROJECT: When a cell phone camera is used to capture an image, in the presence of handshakes or jitter that often occur, the captured image appears blurred. otion -eblurring or blur reduction is thus considered to be a highly desirable feature on cell phone. lthough many motion deblurring algorithms are discussed in the literature, they cannot be employed on a cellphone processor due to its limited memory, space, and processing power. The algorithm introduced in this project is specifically aimed at image blur reduction for deployment on a cellphone processor.The existing motion deblurring algorithms can be grouped into two main categories$ preprocessing and postprocessing algorithms. ost preprocessing algorithms involve hardware techniques, which demand extra hardware to be integrated into a cellphone. /n the other hand, post processing algorithms utili'e an inverse  process of blurring via a point spread function !(0* to obtain a deblurred image. 1n general, blind image deconvolution techniques do not generate visually acceptable image quality unless the motion causing the blur is known and can be parameteri'ed by a specific and often a simple motion model, such as constant velocity motion or linear harmonic motion. The enhancement of a low exposure image can be achieved simply by performing tonal correction. Tonal correction is widely used to adjust the appearance of an image on digital display devices and for photo enhancement .&ere an adaptive tonal correction algorithm is introduced to achieve image blur reduction based on a low exposure image.2  1.3 METHODOLOGY: Tonal corrections are those adjustments and changes you make to the brightness and contrast of your image. *or years, (hotoshop)s workhorse tonalcorrection tools have been the 3evels control and the #urves dialog box, both of which can be applied either destructively or as adjustment layers.The overall approach consisted of taking an image, converting it into its spatial frequencies, developing a point spread function !(0* to filter the image with, and then converting the filtered result back into the spatial domain to see if blur was removed. This was performed in several steps, each of which built from having a greater understanding of the one preceding it. The first step was taking a normal !i.e. not  blurred image, creating a known blurring (0*, and then filtering the image so as to add  blur to it. The next step was removing this blur by various methods, but with the information about the (0* that was used to create the blur. fter that, deblurring was  performed without knowing anything about nature of the blurring (0*, except for its si'e. *inally, an algorithm was developed for removing blur from an already blurry image with no information regarding the blurring (0*.4  2. FUNDAMENTALS OF IMAGE BLUR REDUCTION 2.1 INTRODUCTION TO DIGITAL IMAGE PROCESSING: -igital image processing is an area characteri'ed by the need for extensive experimental work to establish the viability of proposed solutions to a given problem. n important characteristic underlying the design of image processing systems is the significant level of testing 5 experimentation that normally is required before arriving at an acceptable solution. This characteristic implies that the ability to formulate approaches 5quickly prototype candidate solutions generally plays a major role in reducing the cost 5 time required to arrive at a viable system implementation. IMAGE:  n image may be defined as a twodimensional function f!x, y , where x 5 y are spatial coordinates, 5 the amplitude of f at any pair of coordinates !x, y is called the intensity or gray level of the image at that point. When x, y 5 the amplitude values of f are all finite discrete quantities, we call the image a digital image. The field of -1( refers to processing digital image by means of digital computer. -igital image is 6
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