Deep Learning based Radial Blur Estimation and Image Enhancement

Hurakadli, Vaishnavi; Kulkarni, Sujaykumar; Patil, Ujwala; Tabib, Ramesh Ashok; Mudengudi, Uma

doi:10.1109/conecct47791.2019.9012864

Cited by 2 publications

(1 citation statement)

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“…An iterative post-filtering method based on an RNN design was proposed in [ 19 ]. In [ 20 ], the authors employ a CNN-based autoencoder to estimate the radial blur and enhance the deblurred image. In [ 21 ], an image enhancement algorithm based on image-derived graph for weak illumination images is proposed.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement

Schiopu¹,

Munteanu²

2022

Sensors

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The paper proposes a novel post-filtering method based on convolutional neural networks (CNNs) for quality enhancement of RGB/grayscale images and video sequences. The lossy images are encoded using common image codecs, such as JPEG and JPEG2000. The video sequences are encoded using previous and ongoing video coding standards, high-efficiency video coding (HEVC) and versatile video coding (VVC), respectively. A novel deep neural network architecture is proposed to estimate fine refinement details for full-, half-, and quarter-patch resolutions. The proposed architecture is built using a set of efficient processing blocks designed based on the following concepts: (i) the multi-head attention mechanism for refining the feature maps, (ii) the weight sharing concept for reducing the network complexity, and (iii) novel block designs of layer structures for multiresolution feature fusion. The proposed method provides substantial performance improvements compared with both common image codecs and video coding standards. Experimental results on high-resolution images and standard video sequences show that the proposed post-filtering method provides average BD-rate savings of 31.44% over JPEG and 54.61% over HEVC (x265) for RGB images, Y-BD-rate savings of 26.21% over JPEG and 15.28% over VVC (VTM) for grayscale images, and 15.47% over HEVC and 14.66% over VVC for video sequences.

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Section: Related Workmentioning

confidence: 99%