Robust defocus blur identification in the context of blind image quality assessment

Marais, Izak van Zyl; Steyn, Willem J.

doi:10.1016/j.image.2007.06.003

Cited by 21 publications

(6 citation statements)

References 25 publications

(44 reference statements)

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“…There have been many studies on NR FQA in past decades. For example, Marais and Steyn [23] proposed a metric differentiating between in-focus and out-of-focus blur using a variation of the spectral subtraction method. Wu et al [24] proposed a NR method for defocus blur measurement in which Sobel operator is used for edge detection and Radon transform is applied to locate line features.…”

Section: Previous Workmentioning

confidence: 99%

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A new methodology in constructing no-reference focus quality assessment metrics

Song¹,

Liu²

2023

Pattern Recognition

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Section: Previous Workmentioning

confidence: 99%

“…As in-focus and out-of-focus images have different blur level, NR IQA metrics for defocus identification, i.e., NR FQA, have been proposed. Most NR FQA metrics are either sophisticatedly hand-crafted utilizing spatial and spectral characteristics of the images [23][24][25][26], or purely learning-based through AI approaches [27,28].…”

Section: Introductionmentioning

confidence: 99%

A new methodology in constructing no-reference focus quality assessment metrics

Song¹,

Liu²

2023

Pattern Recognition

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“…A lot of no-reference image blur detection techniques have been proposed in the literature [14]. The blur metrics of these techniques are based on various approaches, such as the Haar DWT [15], the Sobel edge detection [16][17][18][19], the image power spectrum [20,21], the DCT [22,23], and a hybrid of curvelet, wavelet, and cosine transforms [24]. The simplest of these approaches is the Haar DWT [15], and it can be implemented on the hardware level without using a lot of resources.…”

Section: Image Blur Detectionmentioning

confidence: 99%

On-the-Fly Parallel Processing IP-Core for Image Blur Detection, Compression, and Chaotic Encryption Based on FPGA

et al. 2021

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This paper presents a 3 in 1 standalone FPGA system which can perform color image blur detection in parallel with compression and encryption. Both blur detection and compression are based on the 3-level Haar wavelet transform, which is used as a common building block to save the resources. The compression is based on performing the hard thresholding scheme followed by the Run Length Encoding (RLE) technique. The encryption is based on the 128-bit Advanced Encryption Standard (AES), which is considered one of the most secure algorithms. Moreover, the modified Lorenz chaotic system is combined with the AES to perform the Cipher Block Chaining (CBC) mode. The proposed system is realized using HDL and implemented using Xilinx on XC5VLX50T FPGA. The system has utilized only 25% of the available slices. Furthermore, the system can achieve a throughput of 3.458 Gbps, which is suitable for realtime applications. To validate the compression performance, the system has been tested with all the standard 256×256 images. It is shown that depending on the amount of details in the image, the system can achieve 30dB PSNR at compression ratios in the range of (0.08-0.38). The proposed system can be integrated with digital cameras to process the captured images on-the-fly prior to transmission or storage. Based on the application, the blurred images can be either marked for future enhancement or simply filtered out.

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“…The original image is assumed to be very similar to the blurred image, and the blurred image can therefore be used as the initial value of the original image; in contrast, the initial values of the PSF are unknown. There are methods of estimating the PSF from the specific "blur" [6,7] but no indications about setting the PSF initial values are given [3]. Thus, in this paper we propose a method of setting the initial values of the PSF by estimation of the PSF band in the spatial frequency domain using the logarithmic amplitude spectrum of a blurred image.…”

Section: Introductionmentioning

confidence: 99%

Estimation of PSF using logarithmic amplitude spectrum and restoration of blurred image

Nagashima

Omatu

Yoshioka

2013

Elect Comm in Japan

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SUMMARYIn this research, it is assumed that the degraded images, which are represented as a convolution of the original images with PSFs (point spread functions), are defocused images by the lens system or the limit of resolution. It has been proposed to estimate both the PSF and the original image by a Bayesian-based iterative method. In the Bayesian-based iterative method of image restoration, we need to set suitably an initial value of the PSF and the original image. The initial value of the original image is a blurred image, since there is only a slight difference between blurred and original images. On the other hand, since there is insufficient information on the PSFs, it is too difficult to set the initial values of the PSFs. If blurred images are represented as convolutions of the original images with the PSFs, the band limit of the blurred image will approach the band limit of the PSF. Therefore, we determine the initial value of the PSF by estimating the band limit of the PSF from the logarithmic amplitude spectrum of the blurred image. Finally, we confirm the effectiveness of the proposed method by simulations and experiments with out-of-focus pictures.

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Robust defocus blur identification in the context of blind image quality assessment

Cited by 21 publications

References 25 publications

A new methodology in constructing no-reference focus quality assessment metrics

A new methodology in constructing no-reference focus quality assessment metrics

On-the-Fly Parallel Processing IP-Core for Image Blur Detection, Compression, and Chaotic Encryption Based on FPGA

Estimation of PSF using logarithmic amplitude spectrum and restoration of blurred image

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