Improvement of Weld Images using MATLAB –A Review

In recent years, gamma rays are been used as an improvement method or a tool along with the combination of image processing technique. It has been giving out better result in the detection of any flaws, or hole in case of the weld metal images and they are being adapted and improved ever since. Image processing is the vast area that has its roots in various fields and some of the important areas, where they have been used are in x-ray image, gamma rays and biometrics (iris & fingerprint) using the template matching technique. The radiography test is done using gamma rays for the given input expecting to display the places that has been affected or the ones with flaws in case of a welded metal, using the met lab technique, such as fuzzy and edge detection methodologies along with the filtering process called a gabor filter which is expected to provide corresponding result which is known as a film that is to give better output. The fuzzy technique is the one that has the attention as of know by all the researches since is known exact result like true or false value it can instead provide a degree of results sounds in between range of values as into help with detection of flaws and noises like the salt and pepper (black and white) error that appears in the image (weld image) this type of error may reduce the quality of weld metal image with a usage of fuzzy method the degree of errors are stated. The edge detection is another technique in image processing that detects the outer surface of the weld image perfectly like the active contours and canny operators, along with its own method and pre-processing are smoothing while segmentation is used to state the different region of the image and how they are split and are helpful to define the region that are with defects. The Gabor filter that is used in edge detection is said filter how the defects of the weld image that is given. Keywords: - Radiographic images, Image近年来，伽马射线（gamma rays）常与图像处理技术结合，作为缺陷检测的改进方法或辅助工具。在焊接金属图像的缺陷、孔洞检测任务中，该方案已展现出更优异的检测效果，并在此基础上不断得到优化与完善。 图像处理是一门覆盖广泛的研究领域，其应用根植于诸多学科方向，其中典型的应用场景包括X射线图像、伽马射线图像，以及采用模板匹配技术的生物特征识别（虹膜与指纹识别）场景。 本次研究针对焊接金属开展伽马射线放射照相检测，采用MATLAB技术实现相关算法，涵盖模糊方法、边缘检测技术，以及Gabor滤波器（Gabor filter）滤波流程，最终可输出等效于传统胶片检测的高质量结果。 模糊方法是当前各研究领域广受关注的技术路径：相较于传统仅能输出真/假二值结果的方案，模糊方法可输出介于某一数值区间内的隶属度结果，可有效检测图像中的缺陷与椒盐（黑白）噪声——这类噪声会降低焊接金属图像的质量，通过模糊方法可对噪声的隶属程度进行量化表征。 边缘检测是图像处理的另一核心技术，可精准提取焊接图像的外轮廓特征，例如主动轮廓法与Canny算子（Canny operators）。边缘检测的配套预处理步骤为图像平滑，而图像分割则用于划分图像的不同区域，明确区域拆分逻辑，辅助定位存在缺陷的目标区域。 用于边缘检测的Gabor滤波器可针对性强化焊接图像中的缺陷特征。 关键词：放射照相图像、图像处理