The nature of statistical learning theory
The nature of statistical learning theory
Enhancement of JPEG-Compressed Images by Re-application of JPEG
Journal of VLSI Signal Processing Systems - Special issue on multimedia signal processing
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Digital Image Restoration
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A PDE-based method for ringing artifact removal on grayscale and color JPEG2000 images
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Image deblocking by the dual adaptive FIR wiener filter and overcomplete representation
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Scale-space method of image ringing estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
The curvelet transform for image denoising
IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Postprocessing of Low Bit-Rate Block DCT Coded Images Based on a Fields of Experts Prior
IEEE Transactions on Image Processing
Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids
IEEE Transactions on Image Processing
Design of Linear Equalizers Optimized for the Structural Similarity Index
IEEE Transactions on Image Processing
Rate Bounds on SSIM Index of Quantized Images
IEEE Transactions on Image Processing
Quality Assessment of Deblocked Images
IEEE Transactions on Image Processing
Fine-Granularity and Spatially-Adaptive Regularization for Projection-Based Image Deblurring
IEEE Transactions on Image Processing
Improved wavelet decoding via set theoretic estimation
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Block artifact reduction using a transform-domain Markov random field model
IEEE Transactions on Circuits and Systems for Video Technology
Efficient Image Deblocking Based on Postfiltering in Shifted Windows
IEEE Transactions on Circuits and Systems for Video Technology
Perceptual Rate-Distortion Optimization Using Structural Similarity Index as Quality Metric
IEEE Transactions on Circuits and Systems for Video Technology
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
IEEE Transactions on Image Processing
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We define the new idea of blind image repair as a process of correcting one or more different and unknown types of distortions afflicting an image. These distortions could introduce linear or non-linear degradations, compression artifacts, noise, etc., or combinations of these. Thus the concept encompasses denoising, deblurring, deblocking, deringing, and other post-acquisition image improvement processes that address distortions. The problem is distortion-blind when the natures of the distortion processes are unknown prior to analyzing the image. Towards solving this problem, we describe a new framework for repairing an image that has undergone an unknown set of distortions, based on identifying the distortion(s) present in the image (if any) and applying possibly multiple distortion-specific image repair algorithms. Our philosophy is based on the principle that the task of general purpose image repair is one of agglomeration, i.e., the algorithm should embody multiple high-performing distortion-specific repair modules such that seamless general purpose image repair is achieved. Our proposed framework - the GEneral-purpose No-reference Image Improver (GENII) - enables the design of algorithms that are blind to distortion type as well as to distortion parameters, and only requires as input the distorted image to be repaired. The GENII framework is modular and easily extensible to image repair problems beyond those considered here. GENII operates by using natural scene statistic models to identify distortion, to perceptually optimize the distortion parameter(s), to assess the quality of the intermediate repaired images, and to perceptually optimize the repair processes. We explain the general purpose image repair framework and one specific realization, dubbed GENII-1, which assumes that the image has been affected by one or more of four possible distortion types.The performance of GENII-1 is evaluated on 4000 distorted images, and shown to deliver substantial improvements in both quantitative and qualitative visual quality.