一种基于模糊识别和估计的有效反卷积技术

Rikita Chokshi, Dippal Israni, Nishidh Chavda
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引用次数: 3

摘要

图像失真是当今最大的挑战。这影响到从摄影到医学成像、天文学、遥感和显微镜等许多领域。由于许多原因,如手的运动和车辆(卫星)的发射引起的振动,图像中的噪声,不利的图像/环境条件以及物体的快速移动,图像会变得模糊。需要一种技术,它可以解决上述问题,并采取可能的步骤,以保持图像模糊尽可能小。在恢复的几个步骤中,模糊检测是任何盲图像恢复所需的首要步骤。本文比较了利用矩不变量、梯度直方图、泽尼克矩等特征从损坏/退化图像中发现模糊类型的各种技术。本文还对不同的线性和非线性恢复技术进行了比较。基于模糊类型、模糊估计、结构相似指数(SSIM)、峰值信噪比(PSNR)进行分析比较。
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An efficient deconvolution technique by identification and estimation of blur
Distortion in images is a biggest challenge now-a-days. This affects in many areas ranging from photography to medical imaging, astronomy, remote sensing and microscopy. Images get obscured due to many reasons like vibration due to hand movement as well as launch of vehicle (Satellite), Noise in image, Adverse Image/Environment condition, and Quick movement of objects. A technique is required which can solve the above mentioned problems and make possible steps to keep image obscureness as minimum as possible. Out of several steps of restoration, blur detection is a primary step required for any blind image restoration. In this paper comparison of various techniques are proposed which finds out type of blur from the corrupted/degraded image using features like Moment Invariants, Histogram of Oriented Gradients, ZernikeMoment. This paper also describes comparison of different linear and nonlinear restoration techniques. The analysis and comparison was yielded out based on types of blur, estimation of blur, Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR).
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