Blur Classification Using Segmentation Based Fractal Texture Analysis

The objective of vision based gesture recognition is to design a system, which can understand the human actions and convey the acquired information with the help of captured images. An image restoration approach is extremely required whenever image gets blur during acquisition process since blurred images can severely degrade the performance of such systems. Image restoration recovers a true image from a degraded version. It is referred as blind restoration if blur information is unidentified. Blur identification is essential before application of any blind restoration algorithm. This paper presents a blur identification approach which categories a hand gesture image into one of the sharp, motion, defocus and combined blurred categories. Segmentation based fractal texture analysis extraction algorithm is utilized for featuring the neural network based classification system. The simulation results demonstrate the preciseness of proposed method

Development of image restoration techniques

Image denoising and image deblurring are studied as part of the thesis. In deblurring, blind deconvolution is investigated. Out of the several classes of blind deconvolution techniques, Non parametric Methods based on Image Constraints are studied at greater depth. A new algorithm based on the Iterative Blind Deconvolution(IBD) technique is developed. The algorithm makes use of spatial domain constraints of non-negativity and support. The Fourier-domain constraint may be described as constraining the product of the Fourier spectra of the image f and the Fourier spectra of the point spread function h to be equal to the convolution spectrum. Within each iteration, the algorithm switches between spatial domain and frequency domain and imposes known constraints on each. The convergence of the original IBD can be accelerated by limiting high magnitude values in frequency domain of both estimated image and point spread function. The new algorithm converges within less than 25 iterations where as the original IBD took nearly 500 iterations. Inclusion of the support constraint in the spatial domain improves quality of the restored image. Also, sum of the spatial domain values of the point spread function should be made equal to one at the end of each iteration, for preventing the loss of image intensity. PSNR values calculated for restored images show signi¯cant improvement in image quality. A PSNR of 17.8dB is obtained for the improved scheme where as it is 14.3dB for the original IBD. A new stopping criteria based on standard deviation of the image power for last k iterations is de¯ned for stopping the algorithm when it converges. In denoising, an edge retrieval technique is developed which preserves the image details along with e®ectively removing impulse noise. Noisy pixels are detected in the ¯rst phase and in the next phase those pixel values are replaced with an estimate of the actual value. For dealing with the wrong classi¯cation of edge pixels as noisy pixels, an edge retrieval technique based on pixel-wise MAD is de¯ned. This scheme retrieves the pixels which are wrongly classi¯ed as noise. The algorithm gives high PSNR values as well as very good detail preservation

A Pattern Classification Based approach for Blur Classification

Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach

Image Blur Detection Using Local Power Spectrum

In this paper work, blur detection of images is carried with local power spectrum. De blurring of image plays a important role in image processing and computer vision techniques. In deblurring of image, the first step is considered the input image as a motion blurred image. Our blur detection is based on block by block local mean calculation. After that find out the global mean for the blurred image, then comparison of local mean with global mean takes place. The experimental result shows that the robustness of proposed algorithm. The proposed method performing operations on image for detecting blurred regions. After that detected blurred content converted in to an un blurred region that shows the final output of this method

Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages.  Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision