A POCS super resolution restoration algorithm based on BM3D

Chen, Jian; Wang, Weiguo; Tingxia, Liu; Zhang, ZhenDong; Hui-bin, Gao

doi:10.1038/s41598-017-15273-0

Cited by 9 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e L 2 -BM3D denoising algorithm was used by Santos et al [28] for ultrasonic image denoising research. e BM3D denoising algorithm was used by Chen et al [29] for the optimization of projection onto convex sets (POCS). e BM3D denoising algorithm was improved by Salehjahromi et al [30] and CT image was reconstructed by using low rank average algorithm.…”

Section: Discussionmentioning

confidence: 99%

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Zhang

Yin

Han

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

This work was aimed to explore the application of the L2-block-matching and 3-dimentional filtering (BM3D) (L2-BM3D) denoising algorithm in the treatment of lumbar degeneration with long- and short-segment fixation of posterior decompression. 120 patients with degenerative lumbar scoliosis were randomly divided into group A (MRI images were not processed), group B (MRI images were processed by the BM3D denoising algorithm), and group C (MRI images were processed by the BM3D denoising algorithm based on weighted norm L2). This denoising algorithm was comprehensively evaluated in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and running time. Besides, the results of surgeries based on different denoising methods were assessed through the surgical time, intraoperative blood loss, postoperative drainage, and postoperative follow-up. The results showed the following: (1) PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) of the L2-BM3D algorithm are better than those of the BM3D algorithm (31.21 dB versus 29.33 dB, 0.83 versus 0.72), while mean square error (MSE) was less than that of the BM3D algorithm ( P < 0.05 ). (2) The operation time, intraoperative bleeding, and postoperative drainage volume in group C were lower than those in group B and group A ( P < 0.05 ). The postoperative follow-up results showed that, in group C, the postoperative VAS (visual analysis scale) score (1.03 ± 0.29) and ODI (Oswestry disability index) (9.29 ± 0.32) were lower, indicating that the postoperative recovery effect of patients was better. Therefore, the patient’s postoperative recovery effect was better. In conclusion, the L2-BM3D algorithm had an ideal denoising effect on MRI images of lumbar degeneration and was worthy of clinical promotion.

show abstract

Section: Discussionmentioning

confidence: 99%

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Zhang

Yin

Han

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…SISR omit the need for an accurately co-registered HR-MSI, but they have their own set of challenges. For instance, PCOS assumes prior knowledge and it is highly sensitive to noise [336]. Additionally, the limitation of regularization methods differ between stochastic and deterministic techniques.…”

Section: B Sisr Challengesmentioning

confidence: 99%

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

Aburaed

Alkhatib

Marshall

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.

show abstract

“…The HCNN method involves a three-step hierarchical procedure based on the edge branch extraction, the edge reinforcement branch, and the SR image reconstruction branch. Prior knowledge and very sensitive to noise issued SR algorithm discussed in [46]. In this approach, the author fuses the information of multi-scale image information in a non-linear manner and uses a cascading-based multi-scale global mechanism to capture the non-local feature information.…”

Section: Related Workmentioning

confidence: 99%

SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution

2023

View full text Add to dashboard Cite

Recent research on image and video processing using convolutional neural networks has shown remarkable improvements, especially in the area of single image super-resolution(SISR). The primary target of SISR is to recover the visually appealing high-resolution (HR) output image from the original degraded low-resolution (LR) input image. However, most recent convolutional neural networks (CNNs)based image super-resolution frameworks often used a deeper and broader network architecture that requires a sizeable computational resource, risk of overfitting, increases computational complexity, and more memory consumption, as well as takes more processing time during the evaluations. To address these issues, we have presented a Squeeze-and-ExcitationNext for Single Image Super-Resolution approach, known as SENext. In brief, the squeeze-and-excitation blocks (SEB) are used in our network architecture with a view to reduce the computational cost and adopt the channel-wise feature mappings to recalibrate the features adaptively. Furthermore, local, sub-local, and global skip connections are employed between each SEB to enable the feature reusability and stabilize training convergence smoothly. Instead of hand-designed bicubic upsampling at pre-processing step, we have performed post-upsampling at the later stage to reconstruct the high-resolution image. Extensive quantitative and qualitative experiments are performed on the benchmark test dataset, including Set5, Set14, BSDS100, Urban100, and Manga109. These experimental evaluations validate the superiority of the SENext over other deep CNN image SR methods in terms of PSNR/SSIM, FLOPs, Number of parameters, processing speed, and visually pleasing effect.

show abstract

A POCS super resolution restoration algorithm based on BM3D

Cited by 9 publications

References 15 publications

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

Denoising of Degenerative Lumbar Spine Lesions MRI Images Using Block-Matching and 3D Filtering

A Review of Spatial Enhancement of Hyperspectral Remote Sensing Imaging Techniques

SENext: Squeeze-and-ExcitationNext for Single Image Super-Resolution

Contact Info

Product

Resources

About