Some Research On Digital Image Processing About Edge Extraction And Denoising

Digital image processing technology originated in the 1920s, up to early 1970s, has formed a more perfect system of discipline. With the constant deepening of the study, people come to realize the edges of the images contain most useful information which is the main media of the image information. It is significant to extract effectively the edges of the images for image segmentation, image recognition and so on. In the actual images, their quality sometimes degenerate seriously as a result of noises. It is no doubt that the studies about the mechanism of the images degeneration, noise characters, how to establish the degradation model as well as how to restore effectively the images will be benefit for the following edge detection work. This dissertation concentrates on the edge detection and the denoising about digital image processing technologies. In edge detection aspect, we proposed an edge detection algorithm based on the directional wavelet transformation and an improved edge detection algorithm based on the Canny operator; In the denoising aspect, a denoising algorithm about the Gauss noise image based on the directional wavelet transformation and a restoring image algorithm about impulse noise based on BP-net and adaptive window switching median filtering are proposed.(1) An Edge Detection Approach Based on Directional Wavelet Transform.We propose an edge detection approach based on directional wavelet transform which retains the separable filtering and the simplicity of compu- tations and filter design from the standard two-dimensional WT. This separable discrete directional wavelet transform is implemented based on lattice theory. Although the transforms can be applied along any directions, only four transform directions {[1,0], [0,1], [1,1], [-1,1]} are chosen because of considering computational complexity and correlation among pixels. In this new transform frame, the corresponding gradient magnitude is redefined. In the process of applying directional wavelet transforms along four directions {[1,0], [1,1], [0,1], [-1,1]}, the eight directions with respect to directional derivative are generated, namely {[1,0],[-1,0]; [1,1],[-1,-1]; [0,1],[0,-1]; [-1,1],[1,-1]}. We find that it is unsuitable to apply non-maximum suppression only along maximal change direction of the derivatives in a number of experiments. In some cases, it is still necessary to choose the secondary change direction of the derivatives. Based on the analysis mentioned above, an new algorithm for non-maximum suppression is described.(2) Improved Image Edge Extraction Algorithm Based on the Canny Operator.In the Canny edge detection algorithm, edges are detected and located by double threshold values, so that it is difficult to choose a reasonable Lower limit threshold value. Choosing a big lower limit threshold will lead to troubles that edges cannot be fully located and are discontinuous; On the contrary, choosing a small lower limit threshold will produce a number of false edges. In addition, the edges which are gained by the traditional Canny algorithm cannot achieve the single pixel level, that is, An edge point corresponds several responses. We have improved the Canny edge detection algorithm. Locate the edge by a method with four threshold values. Then, conduct edge thin operation by introducing into a morphological operator. The improved algorithm can enhance capability of suppressing noise, delete false edges and obtain exact edges. Experimental results have indicated the feasibility and validity of the improved algorithm.(3) A Denoising Algorithm For The Gauss Noise Model Images Base On The Directional Wavelets Transform.At present, wavelet denoising has become one of the main image denoising methods. Based on some understandings and summaries for the current wavelet de-noising literature, we introduce the threshold de-noising and the relevance de-noising methods based on the standard wavelet respectively. Considering that the directional wavelet transform has more ability of directional transform than the traditional wavelet transform and the standard wavelet transform can be seen as a special case of the directional wavelet transform, we popularize the thoughts from the threshold de-noising and the relevance de-noising methods based on the standard wavelet into the directional wavelet. Our scheme is described as followed: Applying wavelet transform along several directional combinations, and then, letting median value of all groups as the final result, which can be considered as processing a rotating image. It is benefit to eliminate so called Gibbs affects and protect the image edges.(4)A Restoring Impulse Noise Image Algorithm Based On BP-Net And Adaptive Window Switching Median Filtering.In order to denoise impulse noises in images, an adaptive window switching median filtering method is proposed. It is different from the traditional median filtering method that our algorithm profits the thoughts from [84, 85, 86, 87, 88, 89, 90, 91], adopt two step schemes including noise detecting and filtering to denoise. Since only noise pixels are filtered, it can be avoided that the restoration image are seriously degenerated. In the stage of the noise detection, an noise detection method based on BP-Net is proposed. Firstly, the BDND algorithm from [92] is improved, which acts as a weak classifier and applies an initial classification for each pixel; and then, it is made a final decision by adopting the BP-Net. The method based on two steps makes the accuracy of the noise detector is improved significantly, which can detect effectively the distribution of the impulse noise in 70% noise density. In the stage of filtering, considering the traits of the noise detection method, a new adaptive window switching median filtering method is proposed. According to result of noise detection, the filter can adjust adaptively window's width and sample choicely, each noisy point in image is denoised by filtering. Two benefits are significant. Firstly, merely signal point involve in filtering treatment, which avoid the interference from the noise points during filtering process; secondly, the filter can adjust adaptively window's width, which can avoid the window is too large or too small to cause image blurring and distortion, protect effectively the edges and details of the images.