Blind Image Deblurring Using Elastic-Net Based Rank Prior

Linearly uncorrelated principal component and deep convolutional image deblurring for natural images

“…In this section, a series of experiments are conducted to verify the efficiency and effectiveness of our deblurring algorithm. For comparison, Image Prior [1] and LRMA [2] Gradient Projection Method [7] and Coupled Learning Algorithm [3] are chosen for deblurring. Tests are conducted using UPenn Natural Image Database [19].…”

“…In order to perform deblurring of natural images uses the proposed LUPC‐DC method and comparison with four other existing methods, Image Prior [1], LRMA [2], Gradient Projection Method [7] and Coupled Learning Algorithm [3] using UPenn Natural Image Database. The UPenn Natural Image Database is accessible at http://tofu.psych.upenn.edu/ [20].…”

“…The objective of image deblurring is to retrieve a comprehensible image and a blur kernel from a blurred input. A novel blind image deblurring called Image Prior was investigated in [1] based on an elastic‐net regularisation of singular values measured from similar patches of an image. Followed by this, an efficient numerical optimisation method was then designed.…”

Real‐time recovery and recognition of motion blurry QR code image based on fractional order deblurring method

“…However, these methods do not generate clear results when the number of step edges is insufficient in the blurry images. In addition, Wang et al [9] proposed an elastic‐net regularisation of singular values computed from the similar patches of an image and used it to direct kernel estimation.…”

“…Image deblurring is a classical image and signal processing problem [1–9]. The formation process of an image blur is typically modelled as follows: where , , , and denote the blurry image, latent image, blur kernel [point spread function (PSF)], and noise, respectively.…”