Abstract: The quality of images is essential in computer vision, image processing, and other related fields. Many digital images contain blurred regions, which are caused by motion or defocus. The blur detection algorithms are found very helpful in real-life applications and therefore have been developed in various multimedia-related research areas, including image restoration, image enhancement, and image segmentation. Image restoration is one of the categories in image processing, where the quality of an image plays a vital role in the process. Blur detection is a pre-processing stage in image restoration. Blur detection techniques are used to remove the blur from a blurred region of an image, which is due to the defocus of a camera or the motion of an object. In this paper we represent some methods of blur detection, such as Blind image de-convolution, Low depth of field, Edge sharpness analysis, and Low directional high frequency energy, Haar Wavelet Transform (HWT), Fast Fourier transform (FFT), Laplacian operator, Modified Laplacian (MLAP), Tenengrad (TEN), Gaussian Blurring, Median Blur, Bilateral Blur. After studying all these techniques, we have found that a lot of future work is required for the development of a perfect and effective blur detection technique.

Keywords: Blur detection, Blur image, Image processing, Blur classification, Blind image deconvolution, Edge sharpness analysis, DOF, HWT, FFT, MLAP, TEN

Downloads: | DOI: 10.17148/IJIREEICE.2025.13924